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Have you ever read a sentence in a book that caught you off guard with its meaning? Maybe it started in one direction and then, suddenly, the meaning changed, making you stumble and re-read it. These are known as garden-path sentences, and they are at the heart of a fascinating study on human cognition—a study that also sheds light on the capabilities of AI, specifically the language model ChatGPT.


Certainly! Here is a comparison table outlining the key aspects of language processing in ChatGPT versus humans based on the study:


Feature ChatGPT Humans
Context Use Utilizes previous context to predict what comes next. Uses prior context and background knowledge to anticipate and integrate new information.
Predictive Capabilities Can predict human memory performance in language-based tasks . Naturally predict and create expectations about upcoming information.
Memory Performance Relatedness ratings by ChatGPT correspond with actual memory performance. Proven correlation between relatedness and memory retention, especially in the presence of fitting context.
Processing Manner Processes information autoregressively, using the preceding context to anticipate future elements . Sequentially processes language, constructing and updating mental models based on predictions.
Error Handling Requires updates in case of discrepancies between predictions and actual information . Creation of breakpoints and new mental models in case of prediction errors.
Cognitive Faculties Lacks an actual memory system, but uses relatedness as a proxy for foreseeing memory retention. Employs cognitive functions to process, comprehend, and remember language-based information.
Language Processing Mimics certain cognitive processes despite not being based on human cognition. Complex interplay of cognitive mechanisms for language comprehension and memory.
Applications Potential to assist in personalized learning and cognitive enhancements, especially in diverse and elderly groups. Continuous learning and cognitive abilities that could benefit from AI-powered enhancement strategies



This comparison table synthesizes the congruencies and distinctions discussed in the research, providing a broad understanding of how ChatGPT and humans process language and the potential for AI-assisted advancements in cognitive performance.

The Intrigue of Garden-Path Sentences

Certainly! Garden-path sentences are a unique and useful tool for linguists and psychologists studying human language processing and memory. These sentences are constructed in a way that initially leads the reader to interpret them incorrectly, often causing confusion or a momentary misunderstanding. The term “garden-path” refers to the idiom “to be led down the garden path,” meaning to be deceived or misled.

Usually, the first part of a garden-path sentence sets up an expectation that is violated by the later part, which forces the reader to go back and reinterpret the sentence structure to make sense of it. This reanalysis process is of great interest to researchers because it reveals how people construct meaning from language, how they deal with syntactic ambiguity, and how comprehension and memory interact.

The classic example given,

“The old man the boat,”

relies on the structural ambiguity of the word “man.”

Initially, “The old man” reads like a noun phrase, leading you to expect a verb to follow.

But as you read “the boat,” confusion arises because “the boat” doesn’t function as a verb.

Here’s where the garden-path effect comes into play:

To make sense of the sentence, you must realize “man” is being used as a verb, meaning to operate or staff, and “the old” functions as the subject. The corrected interpretation is that older individuals are the ones operating the boat.

Other examples of garden-path sentences might include:

  • The horse raced past the barn and fell.” At first read, you might think the sentence is complete after “barn,” making “fell” seem out of place. However, the sentence means the horse that was raced past the barn is the one that fell.
  • The complex houses married and single soldiers and their families.” Initially, “complex” might seem to be an adjective modifying “houses,” but “houses” is in fact a verb, and “the complex” refers to a housing complex.

These sentences demonstrate the cognitive work involved in parsing and understanding language. By examining how people react to and remember such sentences, researchers can gain insights into the psychological processes underlying language comprehension and memory formation

ChatGPT’s Predictive Capability

Garden-path sentences, with their inherent complexity and potential to mislead readers temporarily, have allowed researchers to observe the processes involved in human language comprehension and memory. The study at the core of this discussion aimed to push boundaries further by exploring whether an AI model, specifically ChatGPT, could predict human memory performance concerning these sentences.

The study presented participants with pairs of sentences, where the second sentence was a challenging garden-path sentence, and the first sentence provided context. This context was either fitting, meaning it was supportive and related to the garden-path sentence, making it easier to comprehend, or unfitting, where the context was not supportive and made comprehension more challenging.

ChatGPT, mirroring human cognitive processes to some extent, was used to assess the relatedness of these two sentences and to predict the memorability of the garden-path sentence.

The participants then participated in a memory task to see how well they recalled the garden-path sentences. The correlation between ChatGPT’s predictions and human performance was significant, suggesting that ChatGPT could indeed forecast how well humans would remember sentences based on the context provided.

For instance, if the first sentence was

Jane gave up on the diet,” followed by the garden-path sentence

Eating carrots sticks to your ribs,” the fitting context (“sticks” refers to adhering to a diet plan), makes it easier for both humans and

ChatGPT to make the sentence memorable. On the contrary, an unfitting context like

The weather is changing” would offer no clarity, making the garden-path sentence less memorable due to a lack of relatability.

This reveals the role of context and relatability in language processing and memory. Sentences placed in a fitting context were rated as more memorable and, indeed, better remembered in subsequent tests. This alignment between AI assessments and human memory performance underscores ChatGPT’s predictive capability and the importance of cohesive information in language retention.

Memory Performance in Fitting vs. Unfitting Contexts

In the study under discussion, the experiment involved presenting participants with two types of sentence pairs. Each pair consisted of an initial context-setting sentence (Sentence 1) and a subsequent garden-path sentence (Sentence 2), which is a type of sentence designed to lead the reader to an initial misinterpretation.

In a “fitting” context, the first sentence provided would logically lead into the garden-path sentence, aiding comprehension by setting up the correct framework for interpretation.

For example, if Sentence 1 was “The city has no parks,” and Sentence 2 was “The ducks the children feed are at the lake,” the concept of feed here would fit with the absence of city parks, and the readers can easily understand that “the children feed” is a descriptive action relating to “the ducks.”

Conversely, in an “unfitting” context, the first sentence would not provide a supportive backdrop for the garden-path sentence, making it harder to parse and potentially less memorable.

If Sentence 1 was “John is a skilled carpenter,” and Sentence 2 remained “The ducks the children feed are at the lake,” the relationship between Sentence 1 and Sentence 2 is not clear because carpentry has no apparent connection to feeding ducks or the lake.

Participants in the study were asked to first rate the relatedness of these two sentences on a scale. The study found that participants rated fitting contexts as more related than unfitting ones.

The second part of the task was a surprise memory test where only garden-path sentences were presented, and the participants were required to recall them. It was discovered that the garden-path sentences that had a preceding fitting context were better remembered than those with an unfitting context—this indicated that context plays a critical role in how we process and retain sentences.

ChatGPT, a generative AI system, predicted this outcome. The model also rated garden-path sentences as more memorable when they had a fitting context, similar to human participants, demonstrating its capability to forecast memory performance based on context.

This highlights not only the role of context in human memory but also the potential for AI to predict human cognitive processes.

Stochastic Reasoning: A Potential Cognitive Mechanism

The study in question introduces the notion of stochastic reasoning as a potential cognitive mechanism affecting memory performance. Stochastic reasoning involves a probabilistic approach to understanding the availability of familiar information, also known as retrieval cues, which are instrumental in bolstering memory recall.

The presence of related, coherent information can elevate activation within our cognitive processes, leading to an increased likelihood of recalling that information later on.

Let’s consider an example to elucidate this concept. Imagine you are provided with the following two sentences as part of the study:

“The lawyer argued the case.”
“The evidence was compelling.”

In this case, the two sentences provide a fitting context where the first sentence creates a foundation of understanding related to legal scenarios and the second sentence builds upon that context by introducing “compelling evidence,” which is a familiar concept within the realm of law.

This clear and potent relation between the two sentences forms strong retrieval cues that enhance memory performance, as your brain more easily links “compelling evidence” with “lawyer argued the case,” which aids in later recollection.

Alternatively, if the second sentence was entirely unrelated, such as “The roses in the garden are in full bloom,” the lack of a fitting context would mean weak or absent retrieval cues. As the information related to law does not connect well with the concept of blooming roses, this results in less effective memory performance due to the disjointed nature of the information being processed.

The study found that when sentences are placed within a fitting context that aligns well with our existing knowledge and background, the relationship between the sentences is clear, thus providing stronger cues that streamline the retrieval process and lead to better retention and recall of information.

This reflects the significance of stochastic reasoning and the role of familiarity and coherence in enhancing memory performance.

ChatGPT vs. Human Language Processing

The paragraph delves into the intriguing observation that ChatGPT, a language model developed by OpenAI, and humans share a commonality in how they process language despite the underlying differences in their “operating systems” or cognitive architectures 1. Both seem to rely significantly on the surrounding context to comprehend incoming information and to integrate it coherently with the preceding context.

To illustrate, consider the following example of a garden-path sentence: “The old man the boat.” This sentence is confusing at first because “man” is often used as a verb, and the reader initially interprets “the old man” as a noun phrase.

The confusion is cleared up when provided with a fitting context, such as “elderly people are in control.” Now, the phrase makes sense—’man’ is understood as a verb meaning ‘to staff,’ and the garden-path sentence is interpreted correctly to mean that elderly people are the ones operating the boat.

However, if the preceding sentence was unrelated, such as “The birds flew to the south,” there is no helpful context to parse “The old man the boat” correctly, and it remains confusing, illustrating an unfitting context. This unfitness affects the recall of the garden-path sentence in the memory task, as it lacks clear, coherent links to preexisting knowledge or context that facilitate understanding and later recall.

The study’s findings depicted that when humans assess two sentences as being more related, which is naturally higher in fitting contexts than in unfitting ones, the memory performance for the ambiguous (garden-path) sentence also improves.

In a compelling parallel, ChatGPT generated similar assessments when given the same sentences, assigning higher relatedness values to fitting contexts over unfitting ones. This correlation suggests a similarity in how ChatGPT and humans use context to parse and remember new information.

Furthermore, the relatedness ratings were not just abstract assessments but tied directly to the actual memorability of the sentences. As with humans, ChatGPT’s predictions of memorability were also higher for sentences in fitting contexts, a phenomenon that may stem from its sophisticated language processing capabilities that crudely mimic cognitive processes involved in human memory.

This similarity in the use of context and its impact on memory retention is remarkable, considering the different mechanisms through which humans and machine learning models operate.

Broader Implications and the Future

The paragraph outlines the wider ramifications of the research findings on the predictive capabilities of generative AI like ChatGPT regarding human memory performance in language tasks. The research suggests that these AI models could have practical applications in several domains, including:


AI could be used to tailor learning experiences for students with diverse cognitive needs. By understanding how different students retain information, AI applications could guide educators in adjusting teaching materials, pace, and instructional approaches to cater to individual learning styles and abilities.

For example, if a student is struggling with remembering historical dates, the AI might suggest teaching methods or materials that align with their learning patterns to improve retention.


The study indicates that older adults often face a cognitive slowdown, which could lead to more frequent memory problems. AI, once trained on data taking into account individual cognitive differences, could aid in developing personalized cognitive training and therapy plans aimed at enhancing mental functions in the elderly.

For instance, a cognitive enhancement program might be customized for an older adult who has difficulty recalling names or recent events by using strategies found effective through AI analysis.

Impact of AI on human cognition

The implications here go beyond just predicting human behavior; they extend to potentially improving cognitive processes through the intervention of AI.

These potential applications represent a synergistic relationship between AI and human cognitive research, where the insights gained from one field can materially benefit the other.

Furthermore, adaptive AI systems could continually learn and improve their predictions and recommendations based on new data, thereby creating a dynamic and responsive tool for cognitive enhancement and education.

March 14, 2024

Welcome to the world of open-source (LLMs) large language models, where the future of technology meets community spirit. By breaking down the barriers of proprietary systems, open language models invite developers, researchers, and enthusiasts from around the globe to contribute to, modify, and improve upon the foundational models.

This collaborative spirit not only accelerates advancements in the field but also ensures that the benefits of AI technology are accessible to a broader audience. As we navigate through the intricacies of open-source language models, we’ll uncover the challenges and opportunities that come with adopting an open-source model, the ecosystems that support these endeavors, and the real-world applications that are transforming industries.

Benefits of open-source LLMs

As soon as ChatGPT was revealed, OpenAI’s GPT models quickly rose to prominence. However, businesses began to recognize the high costs associated with closed-source models, questioning the value of investing in large models that lacked specific knowledge about their operations.

In response, many opted for smaller open LLMs, utilizing Retriever-And-Generator (RAG) pipelines to integrate their data, achieving comparable or even superior efficiency.

There are several advantages to closed-source large language models worth considering.

Benefits of Open-Source large language models LLMs

  1. Cost-effectiveness:

Open-source Large Language Models (LLMs) present a cost-effective alternative to their proprietary counterparts, offering organizations a financially viable means to harness AI capabilities.

  • No licensing fees are required, significantly lowering initial and ongoing expenses.
  • Organizations can freely deploy these models, leading to direct cost reductions.
  • Open large language models allow for specific customization, enhancing efficiency without the need for vendor-specific customization services.
  1. Flexibility:

Companies are increasingly preferring the flexibility to switch between open and proprietary (closed) models to mitigate risks associated with relying solely on one type of model.

This flexibility is crucial because a model provider’s unexpected update or failure to keep the model current can negatively affect a company’s operations and customer experience.

Companies often lean towards open language models when they want more control over their data and the ability to fine-tune models for specific tasks using their data, making the model more effective for their unique needs.

  1. Data ownership and control:

Companies leveraging open-source language models gain significant control and ownership over their data, enhancing security and compliance through various mechanisms. Here’s a concise overview of the benefits and controls offered by using open large language models:

Data hosting control:

  • Choice of data hosting on-premises or with trusted cloud providers.
  • Crucial for protecting sensitive data and ensuring regulatory compliance.

Internal data processing:

  • Avoids sending sensitive data to external servers.
  • Reduces the risk of data breaches and enhances privacy.

Customizable data security features:

  • Flexibility to implement data anonymization and encryption.
  • Helps comply with data protection laws like GDPR and CCPA.

Transparency and audibility:

  • The open-source nature allows for code and process audits.
  • Ensures alignment with internal and external compliance standards.

Examples of enterprises leveraging open-source LLMs

Here are examples of how different companies around the globe have started leveraging open language models.

enterprises leveraging open-source LLMs in 2024

  1. VMWare

VMWare, a noted enterprise in the field of cloud computing and digitalization, has deployed an open language model called the HuggingFace StarCoder. Their motivation for using this model is to enhance the productivity of their developers by assisting them in generating code.

This strategic move suggests VMware’s priority for internal code security and the desire to host the model on their infrastructure. It contrasts with using an external system like Microsoft-owned GitHub’s Copilot, possibly due to sensitivities around their codebase and not wanting to give Microsoft access to it

  1. Brave

Brave, the security-focused web browser company, has deployed an open-source large language model called Mixtral 8x7B from Mistral AI for their conversational assistant named Leo, which aims to differentiate the company by emphasizing privacy.

Previously, Leo utilized the Llama 2 model, but Brave has since updated the assistant to default to the Mixtral 8x7B model. This move illustrates the company’s commitment to integrating open LLM technologies to maintain user privacy and enhance their browser’s functionality.

  1. Gab Wireless

Gab Wireless, the company focused on child-friendly mobile phone services, is using a suite of open-source models from Hugging Face to add a security layer to its messaging system. The aim is to screen the messages sent and received by children to ensure that no inappropriate content is involved in their communications. This usage of open language models helps Gab Wireless ensure safety and security in children’s interactions, particularly with individuals they do not know.

  1. IBM

IBM actively incorporates open models across various operational areas.

  • AskHR application: Utilizes IBM’s Watson Orchestration and open language models for efficient HR query resolution.
  • Consulting advantage tool: Features a “Library of Assistants” powered by IBM’s wasonx platform and open-source large language models, aiding consultants.
  • Marketing initiatives: Employs an LLM-driven application, integrated with Adobe Firefly, for innovative content and image generation in marketing.
  1. Intuit

Intuit, the company behind TurboTax, QuickBooks, and Mailchimp, has developed its language models incorporating open LLMs into the mix. These models are key components of Intuit Assist, a feature designed to help users with customer support, analysis, and completing various tasks. The company’s approach to building these large language models involves using open-source frameworks, augmented with Intuit’s unique, proprietary data.

  1. Shopify

Shopify has employed publically available language models in the form of Shopify Sidekick, an AI-powered tool that utilizes Llama 2. This tool assists small business owners with automating tasks related to managing their commerce websites. It can generate product descriptions, respond to customer inquiries, and create marketing content, thereby helping merchants save time and streamline their operations.

  1. LyRise

LyRise, a U.S.-based talent-matching startup, utilizes open language models by employing a chatbot built on Llama, which operates similarly to a human recruiter. This chatbot assists businesses in finding and hiring top AI and data talent, drawing from a pool of high-quality profiles in Africa across various industries.

  1. Niantic

Niantic, known for creating Pokémon Go, has integrated open-source large language models into its game through the new feature called Peridot. This feature uses Llama 2 to generate environment-specific reactions and animations for the pet characters, enhancing the gaming experience by making character interactions more dynamic and context-aware.

  1. Perplexity

Here’s how Perplexity leverages open-source LLMs

  • Response generation process:

When a user poses a question, Perplexity’s engine executes approximately six steps to craft a response. This process involves the use of multiple language models, showcasing the company’s commitment to delivering comprehensive and accurate answers.

In a crucial phase of response preparation, specifically the second-to-last step, Perplexity employs its own specially developed open-source language models. These models, which are enhancements of existing frameworks like Mistral and Llama, are tailored to succinctly summarize content relevant to the user’s inquiry.

The fine-tuning of these models is conducted on AWS Bedrock, emphasizing the choice of open models for greater customization and control. This strategy underlines Perplexity’s dedication to refining its technology to produce superior outcomes.

  • Partnership and API integration:

Expanding its technological reach, Perplexity has entered into a partnership with Rabbit to incorporate its open-source large language models into the R1, a compact AI device. This collaboration facilitated through an API, extends the application of Perplexity’s innovative models, marking a significant stride in practical AI deployment.

  1. CyberAgent

CyberAgent, a Japanese digital advertising firm, leverages open language models with its OpenCALM initiative, a customizable Japanese language model enhancing its AI-driven advertising services like Kiwami Prediction AI. By adopting an open-source approach, CyberAgent aims to encourage collaborative AI development and gain external insights, fostering AI advancements in Japan. Furthermore, a partnership with Dell Technologies has upgraded their server and GPU capabilities, significantly boosting model performance (up to 5.14 times faster), thereby streamlining service updates and enhancements for greater efficiency and cost-effectiveness.

Challenges of open-source LLMs

While open LLMs offer numerous benefits, there are substantial challenges that can plague the users.

  1. Customization necessity:

Open language models often come as general-purpose models, necessitating significant customization to align with an enterprise’s unique workflows and operational processes. This customization is crucial for the models to deliver value, requiring enterprises to invest in development resources to adapt these models to their specific needs.

  1. Support and governance:

Unlike proprietary models that offer dedicated support and clear governance structures, publically available large language models present challenges in managing support and ensuring proper governance. Enterprises must navigate these challenges by either developing internal expertise or engaging with the open-source community for support, which can vary in responsiveness and expertise.

  1. Reliability of techniques:

Techniques like Retrieval-Augmented Generation aim to enhance language models by incorporating proprietary data. However, these techniques are not foolproof and can sometimes introduce inaccuracies or inconsistencies, posing challenges in ensuring the reliability of the model outputs.

  1. Language support:

While proprietary models like GPT are known for their robust performance across various languages, open-source large language models may exhibit variable performance levels. This inconsistency can affect enterprises aiming to deploy language models in multilingual environments, necessitating additional effort to ensure adequate language support.

  1. Deployment complexity:

Deploying publically available language models, especially at scale, involves complex technical challenges. These range from infrastructure considerations to optimizing model performance, requiring significant technical expertise and resources to overcome.

  1. Uncertainty and risk:

Relying solely on one type of model, whether open or closed source, introduces risks such as the potential for unexpected updates by the provider that could affect model behavior or compliance with regulatory standards.

  1. Legal and ethical considerations:

Deploying LLMs entails navigating legal and ethical considerations, from ensuring compliance with data protection regulations to addressing the potential impact of AI on customer experiences. Enterprises must consider these factors to avoid legal repercussions and maintain trust with their users.

  1. Lack of public examples:

The scarcity of publicly available case studies on the deployment of publically available LLMs in enterprise settings makes it challenging for organizations to gauge the effectiveness and potential return on investment of these models in similar contexts.

Overall, while there are significant potential benefits to using publically available language models in enterprise settings, including cost savings and the flexibility to fine-tune models, addressing these challenges is critical for successful deployment

Embracing open-source LLMs: A path to innovation and flexibility

In conclusion, open-source language models represent a pivotal shift towards more accessible, customizable, and cost-effective AI solutions for enterprises. They offer a unique blend of benefits, including significant cost savings, enhanced data control, and the ability to tailor AI tools to specific business needs, while also presenting challenges such as the need for customization and navigating support complexities.

Through the collaborative efforts of the global open-source community and the innovative use of these models across various industries, enterprises are finding new ways to leverage AI for growth and efficiency.

However, success in this endeavor requires a strategic approach to overcome inherent challenges, ensuring that businesses can fully harness the potential of publically available LLMs to drive innovation and maintain a competitive edge in the fast-evolving digital landscape.

February 29, 2024

In the rapidly evolving landscape of technology, small businesses are continually looking for tools that can give them a competitive edge. One such tool that has garnered significant attention is ChatGPT Team by OpenAI.

Designed to cater to small and medium-sized businesses (SMBs), ChatGPT Team offers a range of functionalities that can transform various aspects of business operations. Here are three compelling reasons why your small business should consider signing up for ChatGPT Team, along with real-world use cases and the value it adds.


Read more about how to boost your business with ChatGPT


They promise not to use your business data for training purposes, which is a big plus for privacy. You also get to work together on custom GPT projects and have a handy admin panel to keep everything organized. On top of that, you get access to some pretty advanced tools like DALL·E, Browsing, and GPT-4, all with a generous 32k context window to work with.

The best part? It’s only $25 for each person in your team. Considering it’s like having an extra helping hand for each employee, that’s a pretty sweet deal!


Large language model bootcamp


The official announcement explains:

“Integrating AI into everyday organizational workflows can make your team more productive.

In a recent study by the Harvard Business School, employees at Boston Consulting Group who were given access to GPT-4 reported completing tasks 25% faster and achieved a 40% higher quality in their work as compared to their peers who did not have access.”

Learn more about ChatGPT team

Features of ChatGPT Team

ChatGPT Team, a recent offering from OpenAI, is specifically tailored for small and medium-sized team collaborations. Here’s a detailed look at its features:

  1. Advanced AI Models Access: ChatGPT Team provides access to OpenAI’s advanced models like GPT-4 and DALL·E 3, ensuring state-of-the-art AI capabilities for various tasks.
  2. Dedicated Workspace for Collaboration: It offers a dedicated workspace for up to 149 team members, facilitating seamless collaboration on AI-related tasks.
  3. Administration Tools: The subscription includes administrative tools for team management, allowing for efficient control and organization of team activities.
  4. Advanced Data Analysis Tools: ChatGPT Team includes tools for advanced data analysis, aiding in processing and interpreting large volumes of data effectively.
  5. Enhanced Context Window: The service features a 32K context window for conversations, providing a broader range of data for AI to reference and work with, leading to more coherent and extensive interactions.
  6. Affordability for SMEs: Aimed at small and medium enterprises, the plan offers an affordable subscription model, making it accessible for smaller teams with budget constraints.
  7. Collaboration on Threads & Prompts: Team members can collaborate on threads and prompts, enhancing the ideation and creative process.
  8. Usage-Based Charging: Teams are charged based on usage, which can be a cost-effective approach for businesses that have fluctuating AI usage needs.
  9. Public Sharing of Conversations: There is an option to publicly share ChatGPT conversations, which can be beneficial for transparency or marketing purposes.
  10. Similar Features to ChatGPT Enterprise: Despite being targeted at smaller teams, ChatGPT Team still retains many features found in the more expansive ChatGPT Enterprise version.

These features collectively make ChatGPT Team an adaptable and powerful tool for small to medium-sized teams, enhancing their AI capabilities while providing a platform for efficient collaboration.


Learn to build LLM applications



Enhanced Customer Service and Support

One of the most immediate benefits of ChatGPT Team is its ability to revolutionize customer service. By leveraging AI-driven chatbots, small businesses can provide instant, 24/7 support to their customers. This not only improves customer satisfaction but also frees up human resources to focus on more complex tasks.


Real Use Case:

A retail company implemented ChatGPT Team to manage their customer inquiries. The AI chatbot efficiently handled common questions about product availability, shipping, and returns. This led to a 40% reduction in customer wait times and a significant increase in customer satisfaction scores.


Value for Small Businesses:

  • Reduces response times for customer inquiries.
  • Frees up human customer service agents to handle more complex issues.
  • Provides round-the-clock support without additional staffing costs.

Streamlining Content Creation and Digital Marketing

In the digital age, content is king. ChatGPT Team can assist small businesses in generating creative and engaging content for their digital marketing campaigns. From blog posts to social media updates, the tool can help generate ideas, create drafts, and even suggest SEO-friendly keywords.

Real Use Case:

A boutique marketing agency used ChatGPT Team to generate content ideas and draft blog posts for their clients. This not only improved the efficiency of their content creation process but also enhanced the quality of the content, resulting in better engagement rates for their clients.

Value for Small Businesses:

  • Accelerates the content creation process.
  • Helps in generating creative and relevant content ideas.
  • Assists in SEO optimization to improve online visibility.

Automation of Repetitive Tasks and Data Analysis

Small businesses often struggle with the resource-intensive nature of repetitive tasks and data analysis. ChatGPT Team can automate these processes, enabling businesses to focus on strategic growth and innovation. This includes tasks like data entry, scheduling, and even analyzing customer feedback or market trends.

Real Use Case:

A small e-commerce store utilized ChatGPT Team to analyze customer feedback and market trends. This provided them with actionable insights, which they used to optimize their product offerings and marketing strategies. As a result, they saw a 30% increase in sales over six months.

Value for Small Businesses:

  • Automates time-consuming, repetitive tasks.
  • Provides valuable insights through data analysis.
  • Enables better decision-making and strategy development.


For small businesses looking to stay ahead in a competitive market, ChatGPT Team offers a range of solutions that enhance efficiency, creativity, and customer engagement. By embracing this AI-driven tool, small businesses can not only streamline their operations but also unlock new opportunities for growth and innovation.

January 12, 2024

In the rapidly evolving world of artificial intelligence, OpenAI has marked yet another milestone with the launch of the GPT Store. This innovative platform ushers in a new era for AI enthusiasts, developers, and businesses alike, offering a unique space to explore, create, and share custom versions of ChatGPT models.

The GPT Store is a platform designed to broaden the accessibility and application of AI technologies. It serves as a hub where users can discover and utilize a variety of GPT models.

These models are crafted not only by OpenAI but also by community members, enabling a wide range of applications and customizations.

The store facilitates easy exploration of these models, organized into different categories to suit various needs, such as productivity, education, and lifestyle. Visit chat.openai.com/gpts to explore.


OpenAI GPT Store
Source: CNET


This initiative represents a significant step in democratizing AI technology, allowing both developers and enthusiasts to share and leverage AI advancements in a more collaborative and innovative environment.

In this blog, we will delve into the exciting features of the GPT Store, its potential impact on various sectors, and what it means for the future of AI applications.


Features of GPT Store

The GPT Store by OpenAI offers several notable features:
  1. Platform for custom GPTs: It is an innovative platform where users can find, use, and share custom versions of ChatGPT, also known as GPTs. These GPTs are essentially custom versions of the standard ChatGPT, tailored for a specific purpose and enhanced with their additional information.
  2. Diverse range and weekly highlights: The store features a diverse range of GPTs, developed by both OpenAI’s partners and the broader community. Additionally, it offers weekly highlights of useful and impactful GPTs, serving as a showcase of the best and most interesting applications of the technology.
  3. Availability and enhanced controls: It is accessible to ChatGPT Plus, Teams and Enterprise For these users, the platform provides enhanced administrative controls. This includes the ability to choose how internal-only GPTs are shared and which external GPTs may be used within their businesses.
  4. User-created GPTs: It also empowers subscribers to create their own GPTs, even without any programming expertise.
    For those who want to share a GPT in the store, they are required to save their GPT for everyone and verify their Builder Profile. This facilitates a continuous evolution and enrichment of the platform’s offerings.
  5. Revenue-sharing program: An exciting feature is its planned revenue-sharing program. This program intends to reward GPT creators based on the user engagement their GPTs generate. This feature is expected to provide a new lucrative avenue for them.
  6. Management for team and enterprise customers: It offers special features for Team and Enterprise customers, including private sections with securely published GPTs and enhanced admin controls.

Examples of custom GPTs available on the GPT Store

The earliest featured GPTs on the platform include the following:

  1. AllTrails: This platform offers personalized recommendations for hiking and walking trails, catering to outdoor enthusiasts.
  2. Khan Academy Code Tutor: An educational tool that provides programming tutoring, making learning code more accessible.
  3. Canva: A GPT designed to assist in digital design, integrated into the popular design platform, Canva.
  4. Books: This GPT is tuned to provide advice on what to read and field questions about reading, making it an ideal tool for avid readers.


What is the significance of the GPT Store in OpenAI’s business strategy?

This is a significant component of OpenAI’s business strategy as it aims to expand OpenAI’s ecosystem, stay competitive in the AI industry, and serve as a new revenue source.

The Store likened to Apple’s App Store, is a marketplace that allows users to list personalized chatbots, or GPTs, that they’ve built for others to download.

By offering a range of GPTs developed by both OpenAI business partners and the broader ChatGPT community, this platform democratizes AI technology, making it more accessible and useful to a wide range of users.

Importantly, it is positioned as a potential profit-making avenue for GPT creators through a planned revenue-sharing program based on user engagement. This aspect might foster a more vibrant and innovative community around the platform.

By providing these platforms, OpenAI aims to stay ahead of rivals such as Anthropic, Google, and Meta in the AI industry. As of November, ChatGPT had about 100 million weekly active users and more than 92% of Fortune 500 companies use the platform, underlining its market penetration and potential for growth.

Boost your business with ChatGPT: 10 innovative ways to monetize using AI


Looking ahead: GPT Store’s role in shaping the future of AI

The launch of the platform by OpenAI is a significant milestone in the realm of AI. By offering a platform where various GPT models, both from OpenAI and the community, are available, the AI platform opens up new possibilities for innovation and application across different sectors.

It’s not just a marketplace; it’s a breeding ground for creativity and a step forward in making AI more user-friendly and adaptable to diverse needs.

The potential of the newly launched Store extends far beyond its current offerings. It signifies a future where AI can be more personalized and integrated into various aspects of work and life.

OpenAI’s continuous innovation in the AI landscape, as exemplified by the GPT platform, paves the way for more advanced, efficient, and accessible AI tools. This platform is likely to stimulate further AI advancements and collaborations, enhancing how we interact with technology and its role in solving complex problems.
This isn’t just a product; it’s a gateway to the future of AI, where possibilities are as limitless as our imagination.
January 10, 2024

With the advent of language models like ChatGPT, improving your data science skills has never been easier. 

Data science has become an increasingly important field in recent years, as the amount of data generated by businesses, organizations, and individuals has grown exponentially.

With the help of artificial intelligence (AI) and machine learning (ML), data scientists are able to extract valuable insights from this data to inform decision-making and drive business success.

However, becoming a skilled data scientist requires a lot of time and effort, as well as a deep understanding of statistics, programming, and data analysis techniques. 

ChatGPT is a large language model that has been trained on a massive amount of text data, making it an incredibly powerful tool for natural language processing (NLP).


Uses of generative AI for data scientists

Generative AI can help data scientists with their projects in a number of ways.

Test your knowledge of generative AI



Data cleaning and preparation

Generative AI can be used to clean and prepare data by identifying and correcting errors, filling in missing values, and deduplicating data. This can free up data scientists to focus on more complex tasks.

Example: A data scientist working on a project to predict customer churn could use generative AI to identify and correct errors in customer data, such as misspelled names or incorrect email addresses. This would ensure that the model is trained on accurate data, which would improve its performance.

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Feature engineering

Generative AI can be used to create new features from existing data. This can help data scientists to improve the performance of their models.

Example: A data scientist working on a project to predict fraud could use generative AI to create a new feature that represents the similarity between a transaction and known fraudulent transactions. This feature could then be used to train a model to predict whether a new transaction is fraudulent.

Read more about feature engineering

Model development

Generative AI can be used to develop new models or improve existing models. For example, generative AI can be used to generate synthetic data to train models on, or to develop new model architectures.

Example: A data scientist working on a project to develop a new model for image classification could use generative AI to generate synthetic images of different objects. This synthetic data could then be used to train the model, even if there is not a lot of real-world data available.

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Model evaluation

Generative AI can be used to evaluate the performance of models on data that is not used to train the model. This can help data scientists to identify and address any overfitting in the model.

Example: A data scientist working on a project to develop a model for predicting customer churn could use generative AI to generate synthetic data of customers who have churned and customers who have not churned.

This synthetic data could then be used to evaluate the model’s performance on unseen data.

Master ChatGPT plugins

Communication and explanation

Generative AI can be used to communicate and explain the results of data science projects to non-technical audiences. For example, generative AI can be used to generate text or images that explain the predictions of a model.

Example: A data scientist working on a project to predict customer churn could use generative AI to generate a report that explains the factors that are most likely to lead to customer churn. This report could then be shared with the company’s sales and marketing teams to help them to develop strategies to reduce customer churn.


How to use ChatGPT for Data Science projects

With its ability to understand and respond to natural language queries, ChatGPT can be used to help you improve your data science skills in a number of ways. Here are just a few examples: 


Data science projects to build your portfolio – Data Science Dojo

Answering data science-related questions 

One of the most obvious ways in which ChatGPT can help you improve your data science skills is by answering your data science-related questions.

Whether you’re struggling to understand a particular statistical concept, looking for guidance on a programming problem, or trying to figure out how to implement a specific ML algorithm, ChatGPT can provide you with clear and concise answers that will help you deepen your understanding of the subject. 


Providing personalized learning resources 

In addition to answering your questions, ChatGPT can also provide you with personalized learning resources based on your specific interests and skill level.


Read more about ChatGPT plugins


For example, if you’re just starting out in data science, ChatGPT can recommend introductory courses or tutorials to help you build a strong foundation. If you’re more advanced, ChatGPT can recommend more specialized resources or research papers to help you deepen your knowledge in a particular area. 


Offering real-time feedback 

Another way in which ChatGPT can help you improve your data science skills is by offering real-time feedback on your work.

For example, if you’re working on a programming project and you’re not sure if your code is correct, you can ask ChatGPT to review your code and provide feedback on any errors or issues it finds. This can help you catch mistakes early on and improve your coding skills over time. 



Generating data science projects and ideas 

Finally, ChatGPT can also help you generate data science projects and ideas to work on. By analyzing your interests, skill level, and current knowledge, ChatGPT can suggest project ideas that will challenge you and help you build new skills.

Additionally, if you’re stuck on a project and need inspiration, ChatGPT can provide you with creative ideas or alternative approaches that you may not have considered. 


Improve your data science skills with generative AI

In conclusion, ChatGPT is an incredibly powerful tool for improving your data science skills. Whether you’re just starting out or you’re a seasoned professional, ChatGPT can help you deepen your understanding of data science concepts, provide you with personalized learning resources, offer real-time feedback on your work, and generate new project ideas.

By leveraging the power of language models like ChatGPT, you can accelerate your learning and become a more skilled and knowledgeable data scientist. 


November 10, 2023

ChatGPT made a significant market entrance, shattering records by swiftly reaching 100 million monthly active users in just two months. Its trajectory has since been on a consistent growth. Notably, ChatGPT has embraced a range of plugins that extend its capabilities, enabling users to do more than merely generate textual responses. 


What are ChatGPT Plugins? 

ChatGPT plugins serve as supplementary features that amplify the functionality of ChatGPT. These plugins are crafted by third-party developers and are readily accessible in the ChatGPT plugins store. 

ChatGPT plugins can be used to extend the capabilities of ChatGPT in a variety of ways, such as: 

  • Accessing and processing external data 
  • Performing complex computations 
  • Using third-party services 

In this article, we’ll dive into the top 6 ChatGPT plugins tailored for data science. These plugins encompass a wide array of functions, spanning tasks such as web browsing, automation, code interpretation, and streamlining workflow processes. 


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1. Wolfram 

The Wolfram plugin for ChatGPT is a powerful tool that makes ChatGPT smarter by giving it access to the Wolfram Alpha Knowledgebase and Wolfram programming language. This means that ChatGPT can now perform complex computations, access real-time data, and generate visualizations, all from within ChatGPT. 


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Here are some of the things that the Wolfram plugin for ChatGPT can do: 

  • Perform complex computations: You can ask ChatGPT to calculate the factorial of a large number or to find the roots of a polynomial equation. ChatGPT can also use Wolfram Language to perform more complex tasks, such as simulating physical systems or training machine learning models. Here’s an example of Wolfram enabling ChatGPT to solve complex integrations. 


Wolfram - complex computations

Source: Stephen Wolfram Writings 


  • Generate visualizations: You can ask ChatGPT to generate a plot of a function or to create a map of a specific region. ChatGPT can also use Wolfram Language to create more complex visualizations, such as interactive charts and 3D models. 


Wolfram - Visualization

Source: Stephen Wolfram Writings 


Read this blog to Master ChatGPT cheatsheet

2. Noteable: 

The Noteable Notebook plugin for ChatGPT is a powerful tool that makes it possible to use ChatGPT within the Noteable computational notebook environment. This means that you can use natural language prompts to perform advanced data analysis tasks, generate visualizations, and train machine learning models without the need for complex coding knowledge. 

Here are some examples of how you can use the Noteable Notebook plugin for ChatGPT: 

  • Exploratory Data Analysis (EDA): You can use the plugin to generate descriptive statistics, create visualizations, and identify patterns in your data. 
  • Deploy machine learning Models:  You can use the plugin to train and deploy machine learning models. This can be useful for tasks such as classification, regression, and forecasting. 
  • Data manipulation: You can use the plugin to perform data cleaning, transformation, and feature engineering tasks. 
  • Data visualization: You can use the plugin to create interactive charts, maps, and other visualizations. 

Here’s an example of a Noteable plugin enabling ChatGPT to help perform geospatial analysis: 




Source: Noteable.io 

3. Code Interpreter 

ChatGPT Code Interpreter is a part of ChatGPT that allows you to run Python code in a live working environment. With Code Interpreter, you can perform tasks such as data analysis, visualization, coding, math, and more. You can also upload and download files to and from ChatGPT with this feature. To use Code Interpreter, you must have a “ChatGPT Plus” subscription and activate the plugin in the settings. 

Here’s an example of data visualization through Code Interpreter. 

code interpreter


4. ChatWithGit

ChatWithGit is a ChatGPT plugin that allows you to search for code on GitHub repositories using natural language queries. It is a powerful tool that can help you find code quickly and easily, even if you are not familiar with the codebase. 

To use ChatWithGit, you first need to install the plugin. You can do this by following the instructions on the ChatWithGit GitHub page. Once the plugin is installed, you can start using it to search for code by simply typing a natural language query into the ChatGPT chat box. 

For example, you could type “find Python code for web scraping” or “find JavaScript code for sorting an array.” ChatGPT will then query the Chat with Git plugin, which will return a list of code results from GitHub repositories. 


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5. Zapier 

The Zapier plugin allows you to connect ChatGPT with other cloud-based applications, automating workflows and integrating data. This can be useful for data scientists who need to streamline their data science pipeline or automate repetitive tasks. 

For example, you can use Zapier to automatically trigger a data pipeline in ChatGPT when a new dataset is uploaded to Google Drive or to automatically send a notification to Slack when a machine learning model finishes training. 

Here’s a detailed article on how you can use Zapier for automating tasks using ChatGPT: 

6 ways to use the Zapier ChatGPT Plugin 


6. ScholarAI 

The ScholarAI plugin is designed to help people with academic and research-related tasks. It provides access to a vast database of scholarly articles and books, as well as tools for literature review and data analysis. 

For example, you could use ScholarAI to identify relevant research papers on a given topic or to extract data from academic papers and generate citations. 



Source: ScholarAI 

Experiment with ChatGPT now!

From computational capabilities to code interpretation and automation, ChatGPT is now a versatile tool spanning data science, coding, academic research, and workflow automation. This journey marks the rise of an AI powerhouse, promising continued innovation and utility in the realm of AI-powered assistance 


October 2, 2023


The evolution of the GPT Series culminates in ChatGPT, delivering more intuitive and contextually aware conversations than ever before.


What are chatbots?  

AI chatbots are smart computer programs that can process and understand users’ requests and queries in voice and text. It mimics and generates responses in a human conversational manner. AI chatbots are widely used today from personal assistance to customer service and much more. They are assisting humans in every field making the work more productive and creative. 

Deep learning And NLP

Deep Learning and Natural Language Processing (NLP) are like best friends in the world of computers and language. Deep Learning is when computers use their brains, called neural networks, to learn lots of things from a ton of information.

NLP is all about teaching computers to understand and talk like humans. When Deep Learning and NLP work together, computers can understand what we say, translate languages, make chatbots, and even write sentences that sound like a person. This teamwork between Deep Learning and NLP helps computers and people talk to each other better in the most efficient manner.  

Chatbots and ChatGPT
Chatbots and ChatGPT

How are chatbots built? 

Building Chatbots involves creating AI systems that employ deep learning techniques and natural language processing to simulate natural conversational behavior.

The machine learning models are trained on huge datasets to figure out and process the context and semantics of human language and produce relevant results accordingly. Through deep learning and NLP, the machine can recognize the patterns from text and generate useful responses. 

Transformers in chatbots 

Transformers are advanced models used in AI for understanding and generating language. This efficient neural network architecture was developed by Google in 2015. They consist of two parts: the encoder, which understands input text, and the decoder, which generates responses.

The encoder pays attention to words’ relationships, while the decoder uses this information to produce a coherent text. These models greatly enhance chatbots by allowing them to understand user messages (encoding) and create fitting replies (decoding).

With Transformers, chatbots engage in more contextually relevant and natural conversations, improving user interactions. This is achieved by efficiently tracking conversation history and generating meaningful responses, making chatbots more effective and lifelike. 


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GPT Series – Generative pre trained transformer 

 GPT is a large language model (LLM) which uses the architecture of Transformers. I was developed by OpenAI in 2018. GPT is pre-trained on a huge amount of text dataset. This means it learns patterns, grammar, and even some reasoning abilities from this data. Once trained, it can then be “fine-tuned” on specific tasks, like generating text, answering questions, or translating languages.

This process of fine-tuning comes under the concept of transfer learning. The “generative” part means it can create new content, like writing paragraphs or stories, based on the patterns it learned during training. GPT has become widely used because of its ability to generate coherent and contextually relevant text, making it a valuable tool in a variety of applications such as content creation, chatbots, and more.  

The advent of ChatGPT: 

ChatGPT is a chatbot designed by OpenAI. It uses the “Generative Pre-Trained Transformer” (GPT) series to chat with the user analogously as people talk to each other. This chatbot quickly went viral because of its unique capability to learn complications of natural language and interactions and give responses accordingly.

ChatGPT is a powerful chatbot capable of producing relevant answers to questions, text summarization, drafting creative essays and stories, giving coded solutions, providing personal recommendations, and many other things. It attracted millions of users in a noticeably short period. 

ChatGPT’s story is a journey of growth, starting with earlier versions in the GPT series. In this blog, we will explore how each version from the series of GPT has added something special to the way computers understand and use language and how GPT-3 serves as the foundation for ChatGPT’s innovative conversational abilities. 

Chat GPT Series evolution
Chat GPT Series evolution


GPT-1 was the first model of the GPT series developed by OpenAI. This innovative model demonstrated the concept that text can be generated using transformer design. GPT-1 introduced the concept of generative pre-training, where the model is first trained on a broad range of text data to develop a comprehensive understanding of language. It consisted of 117 million parameters and produced much more coherent results as compared to other models of its time. It was the foundation of the GPT series, and it paved a path for advancement and revolution in the domain of text generation. 


GPT-2 was much bigger as compared to GPT-1 trained on 1.5 billion parameters. It makes the model have a stronger grasp of the context and semantics of real-world language as compared to GPT-1. It introduces the concept of “Task conditioning.” This enables GTP-2 to learn multiple tasks within a single unsupervised model by conditioning its outputs on both input and task information.

GPT-2 highlighted zero-shot learning by carrying out tasks without prior examples, solely guided by task instructions. Moreover, it achieved remarkable zero-shot task transfer, demonstrating its capacity to seamlessly comprehend and execute tasks with minimal or no specific examples, highlighting its adaptability and versatile problem-solving capabilities. 

As the ChatGPT model was getting more advanced it started to have new qualities of writing long creative essays, answering complex questions instead of just predicting the next word. So, it was becoming more human-like and attracted many users for their day-to-day tasks. 


GPT-3 was trained on an even larger dataset and has 175 billion parameters. It gives a more natural-looking response making the model conversational. It was better at common sense reasoning than the earlier models. GTP-3 can not only generate human-like text but is also capable of generating programming code snippets providing more innovative solutions. 

GPT-3’s enhanced capacity, compared to GPT-2, extends its zero-shot and few-shot learning capabilities. It can give relevant and accurate solutions to uncommon problems, requiring training on minimal examples or even performing without prior training.  

Instruct GPT: 

An improved version of GPT-3 also known as InstructGPT(GPT-3.5) produces results that align with human expectations. It uses a “Human Feedback Model” to make the neural network respond in a way that is according to real-world expectations.

It begins by creating a supervised policy via demonstrations on input prompts. Comparison data is then collected to build a reward model based on human-preferred model outputs. This reward model guides the fine-tuning of the policy using Proximal Policy Optimization.

Iteratively, the process refines the policy by continuously collecting comparison data, training an updated reward model, and enhancing the policy’s performance. This iterative approach ensures that the model progressively adapts to preferences and optimizes its outputs to align with human expectations. The figure below gives a clearer depiction of the process discussed. 

Training language models
From Research paper ‘Training language models to follow instructions with human feedback’

GPT-3.5 stands as the default model for ChatGPT, while the GPT-3.5-Turbo Model empowers users to construct their own custom chatbots with similar abilities as ChatGPT. It is worth noting that large language models like ChatGPT occasionally generate responses that are inaccurate, impolite, or not helpful.

This is often due to their training in predicting subsequent words in sentences without always grasping the context. To remedy this, InstructGPT was devised to steer model responses toward better alignment with user preferences.


Read more –> FraudGPT: Evolution of ChatGPT into an AI weapon for cybercriminals in 2023


GPT-4 and beyond: 

After GTP-3.5 comes GPT-4. According to some resources, GPT-4 is estimated to have 1.7 trillion parameters. These enormous number of parameters make the model more efficient and make it able to process up to 25000 words at once.

This means that GPT-4 can understand texts that are more complex and realistic. The model has multimodal capabilities which means it can process both images and text. It can not only interpret the images and label them but can also understand the context of images and give relevant suggestions and conclusions. The GPT-4 model is available in ChatGPT Plus, a premium version of ChatGPT. 

So, after going through the developments that are currently done by OpenAI, we can expect that OpenAI will be making more improvements in the models in the coming years. Enabling it to handle voice commands, make changes to web apps according to user instruction, and aid people in the most efficient way that has never been done before. 

Watch: ChatGPT Unleashed: Live Demo and Best Practices for NLP Applications 


This live presentation from Data Science Dojo gives more understanding of ChatGPT and its use cases. It demonstrates smart prompting techniques for ChatGPT to get the desired responses and ChatGPT’s ability to assist with tasks like data labeling and generating data for NLP models and applications. Additionally, the demo acknowledges the limitations of ChatGPT and explores potential strategies to overcome them.  

Wrapping up: 

ChatGPT developed by OpenAI is a powerful chatbot. It uses the GPT series as its neural network, which is improving quickly. From generating one-liner responses to generating multiple paragraphs with relevant information, and summarizing long detailed reports, the model is capable of interpreting and understanding visual inputs and generating responses that align with human expectations.

With more advancement, the GPT series is getting more grip on the structure and semantics of the human language. It not only relies on its training information but can also use real-time data given by the user to generate results. In the future, we expect to see more breakthrough advancements by OpenAI in this domain empowering this chatbot to assist us in the most effective manner like ever before. 


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September 13, 2023

Master ChatGPT to automate repetitive tasks, including answering frequently asked questions, allowing businesses to provide efficient and round-the-clock customer support. It assists in generating content such as articles, blog posts, and product descriptions, saving time and resources for content creation.

AI-driven chatbots like ChatGPT can analyze customer data to provide personalized marketing recommendations and engage customers in real time. By automating various tasks and processes, businesses can reduce operational costs and allocate resources to more strategic activities.

Key use cases:

 1. Summarizing: ChatGPT is highly effective at summarizing long texts, transcripts, articles, and reports. It can condense lengthy content into concise summaries, making it a valuable tool for quickly extracting key information from extensive documents.

Prompt Example: “Please summarize the key findings from this 20-page research report on climate change.”

2. Brainstorming: ChatGPT assists in generating ideas, outlines, and new concepts. It can provide creative suggestions and help users explore different angles and approaches to various topics or projects.

Prompt Example: “Generate ideas for a marketing campaign promoting our new product.”

3. Synthesizing: This use case involves extracting insights and takeaways from the text. ChatGPT can analyze and consolidate information from multiple sources, helping users distill complex data into actionable conclusions.

Prompt Example: “Extract the main insights and recommendations from this business strategy document.”

4. Writing: ChatGPT can be a helpful tool for writing tasks, including blog posts, articles, press releases, and procedures. It can provide content suggestions, help with structuring ideas, and even generate draft text for various purposes.

Prompt Example: “Write a blog post about the benefits of regular exercise and healthy eating.”

5. Coding: For coding tasks, ChatGPT can assist in writing scripts and small programs. It can help with generating code snippets, troubleshooting programming issues, and offering coding-related advice.

Prompt Example: “Create a Python script that calculates the Fibonacci sequence up to the 20th term.”

6. Extracting: ChatGPT is capable of extracting data and patterns from messy text. This is particularly useful in data mining and analysis, where it can identify relevant information and relationships within unstructured text data.

Prompt Example: “Extract all email addresses from this unstructured text data.”

7. Reformatting: Another valuable use case is reformatting text or data from messy sources into structured formats or tables. ChatGPT can assist in converting disorganized information into organized and presentable formats.

Prompt Example: “Convert this messy financial data into a structured table with columns for date, transaction type, and amount.”


Read more about -> 10 innovative ways to monetize business using ChatGPT


Tones used in ChatGPT prompts

Tone: [x] Writing using [x] tone

1. Conversational

Description: Conversational tone is friendly, informal, and resembles everyday spoken language. It’s suitable for casual interactions and discussions.

Example prompt: “Can you explain the concept of blockchain technology in simple terms?”

2. Lighthearted

Description: Lighthearted tone adds a touch of humor, playfulness, and positivity to the content. It’s engaging and cheerful.

Example prompt: “Tell me a joke to brighten my day.”

3. Persuasive

Description: Persuasive tone aims to convince or influence the reader. It uses compelling language to present arguments and opinions.

Example prompt: “Write a persuasive article on the benefits of renewable energy.”

4. Spartan

Description: Spartan tone is minimalist and to the point. It avoids unnecessary details and focuses on essential information.

Example prompt: “Provide a brief summary of the key features of the new software update.”

5. Formal

Description: Formal tone is professional, structured, and often used in academic or business contexts. It maintains a serious and respectful tone.

Example prompt: “Compose a formal email to inquire about job opportunities at your company.”

6. Firm

Description: Firm tone is assertive and direct. It’s used when a clear and authoritative message needs to be conveyed.

Example prompt: “Draft a letter of complaint regarding the recent service issues with our internet provider.”

These tones can be adjusted to suit specific communication goals and audiences, offering a versatile way to interact with ChatGPT effectively in various situations.


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The format of prompts used in ChatGPT plays a crucial role in obtaining desired responses. Here are different formatting styles and their descriptions:

1. Be concise. Minimize excess prose

Description: This format emphasizes brevity and clarity. Avoid long-winded questions and get to the point.

Example: “Explain the concept of photosynthesis.”

2. Use less corporate jargon

Description: Simplify language and avoid technical or business-specific terms for a more understandable response.

Example: “Describe our company’s growth strategy without using industry buzzwords.”

3. Output as bullet points in short sentences

Description: Present prompts in a bullet-point format with short and direct sentences, making it easy for ChatGPT to understand and respond.


  • “Benefits of recycling:”
  • “Reduces pollution.”
  • “Conserves resources.”
  • “Saves energy.”


4. Output as a table with columns: (x). (y), (z). [a]

Description: Format prompts as a table with specified columns and content in a structured manner.


Item Quantity Price
Apple 5 $1.50
Banana 3 $0.75

5. Be extremely detailed

Description: Request comprehensive and in-depth responses with all relevant information.

Example: “Provide a step-by-step guide on setting up a home theater system, including product recommendations and wiring diagrams.”

Using these prompt formats effectively can help you receive more accurate and tailored responses from ChatGPT, improving the quality of information and insights provided. It’s essential to choose the right format based on your communication goals and the type of information you need


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Chained prompting

Chained prompting is a technique used with ChatGPT to break down complex tasks into multiple sequential steps, guiding the AI model to provide detailed and structured responses. In the provided example, here’s how chained prompting works:

1. Write an article about ChatGPT.

This is the initial prompt, requesting an article on a specific topic.

2. First give me the outline, which consists of a headline, a teaser, and several subheadings.

In response to the first prompt, ChatGPT is instructed to provide the outline of the article, which includes a headline, teaser, and subheadings.

[Output]: ChatGPT generates the outline as requested.

3. Now write 5 different subheadings.

After receiving the outline, the next step is to ask ChatGPT to generate five subheadings for the article.

[Output]: ChatGPT provides five subheadings for the article.

4. Add 5 keywords for each subheading.

Following the subheadings, ChatGPT is directed to add five keywords for each subheading to enhance the article’s SEO and content structure.

[Output]: ChatGPT generates keywords for each of the subheadings.

Chained prompting allows users to guide ChatGPT through a series of related tasks, ensuring that the generated content aligns with specific requirements. It’s a valuable technique for obtaining well-structured and detailed responses from the AI model, making it useful for tasks like content generation, outlining, and more.

This approach helps streamline the content creation process, starting with a broad request and progressively refining it until the desired output is achieved.

Prompts for designers

The prompts provided are designed to assist designers in various aspects of their work, from generating UI design requirements to seeking advice on conveying specific qualities through design. Here’s a description of each prompt:

1. Generate examples of UI design requirements for a [mobile app].

This prompt seeks assistance in defining UI design requirements for a mobile app. It helps designers outline the specific elements and features that should be part of the app’s user interface.

Example: UI design requirements for a mobile app could include responsive layouts, intuitive navigation, touch-friendly buttons, and accessible color schemes.

2. How can I design a [law firm website] in a way that conveys [trust and authority].

This prompt requests guidance on designing a law firm website that effectively communicates trust and authority, two essential qualities in the legal field.

Example: Design choices like a professional color palette, clear typography, client testimonials, and certifications can convey trust and authority.

3. What are some micro-interactions to consider when designing fintech app.

This prompt focuses on micro-interactions, small animations or feedback elements in a fintech app’s user interface that enhance user experience.

Example: Micro-interactions in a fintech app might include subtle hover effects on financial data, smooth transitions between screens, or informative tooltips.

4. Create a text-based excel sheet to input your copy suggestions. Assume you have 3 members in your UX writing team.

This prompt instructs the creation of a text-based Excel sheet for collaborative copywriting among a UX writing team.

Example: The Excel sheet can have columns for copy suggestions, status (e.g., draft, approved), author names, and deadlines, facilitating efficient content collaboration.

These prompts are valuable tools for designers, providing a structured approach to seeking assistance and generating ideas, whether it’s for UI design, conveying specific qualities, considering micro-interactions, or managing collaborative writing efforts. They help streamline the design process and ensure designers receive relevant and actionable guidance


These modes are designed to guide interactions with an AI, such as ChatGPT, in various ways, allowing users to leverage AI in different roles. Let’s describe each of these modes with examples:

1. Intern: “Come up with new fundraising ideas.”

In this mode, the AI acts as an intern, tasked with generating fresh ideas.

Example: Requesting fundraising ideas for a cause or organization.

2. Thought Partner: “What should we think about when generating new fundraising ideas?”

When set as a thought partner, the AI helps users brainstorm and consider key aspects of a task.

Example: Seeking guidance on the critical factors to consider when brainstorming fundraising ideas.

3. Critic: “Here’s a list of 10 fundraising ideas I created. Are there any I missed? Which ones seem particularly good or bad?”

In critic mode, the AI evaluates and provides feedback on a list of ideas or concepts.

Example: Requesting a critique of a list of fundraising ideas and identifying strengths and weaknesses.

4. Teacher: “Teach me about [xl. Assume I know [x] and adjust your language.”

This mode transforms the AI into a teacher, providing explanations and information.

Example: Asking the AI to teach a topic, adjusting the complexity of the language based on the user’s knowledge.


Read more about -> Prompt Engineering 


Prompts for marketers

These prompts are designed to assist marketers in various aspects of their work, from content creation to product descriptions and marketing strategies. Let’s describe each prompt and provide examples where necessary:

1. Can you provide me with some ideas for blog posts about [topics]?

This prompt seeks content ideas for blog posts, helping marketers generate engaging and relevant topics for their audience.

Example: Requesting blog post ideas about “content marketing strategies.”

2. Write a product description for my product or service or company.

This prompt is aimed at generating compelling product or service descriptions, essential for marketing materials.

Example: Asking for a product description for a new smartphone model.

3. Suggest inexpensive ways I can promote my [company] without using social media.”

This prompt focuses on cost-effective marketing strategies outside of social media to increase brand visibility.

Example: Seeking low-cost marketing ideas for a small bakery without using social media.

4. How can I obtain high-quality backlinks to raise the SEO of [website name]?

Here, the focus is on improving website SEO by acquiring authoritative backlinks, a crucial aspect of digital marketing.

Example: Inquiring about strategies to gain high-quality backlinks for an e-commerce website.

These prompts provide marketers with AI-driven assistance for a range of marketing tasks, from content creation to SEO optimization and cost-effective promotion strategies. They facilitate more efficient and creative marketing efforts.


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Prompts for developers

These prompts are designed to assist developers in various aspects of their work, from coding to debugging and implementing specific website features. Let’s describe each prompt and provide examples where needed:

1. Develop architecture and code for a (descriptions website with JavaScript.

This prompt asks developers to create both the architectural design and code for a website that likely involves presenting various descriptions using JavaScript.

Example: Requesting the development of a movie descriptions website with JavaScript.

2. Help me find mistakes in the following code <paste code below>>.

This prompt seeks assistance in identifying errors or bugs in a given piece of code that the developer will paste.

Example: Pasting a JavaScript code snippet with issues and asking for debugging help.

3. I want to implement a sticky header on my website. Can you provide an example using CSS and JavaScript?

Here, the developer requests an example of implementing a sticky (fixed-position) header on a website using a combination of CSS and JavaScript.

Example: Asking for a code example to create a sticky navigation bar for a webpage.

4. Please continue writing this code for JavaScript <post code below>>.

This prompt is for extending an existing JavaScript code snippet by providing additional code to complete a specific task.

Example: Extending JavaScript code for a form validation feature.

These prompts offer valuable assistance to developers, covering a range of tasks from website architecture and coding to debugging and implementing interactive features using JavaScript and CSS. They aim to streamline the development process and resolve coding challenges.

These modes offer flexibility in how users interact with AI, enabling them to tap into AI capabilities for various purposes, including idea generation, brainstorming, evaluation, and learning. They facilitate productive and tailored interactions with AI, making it a versatile tool for a wide range of tasks and roles.


Master ChatGPT to upscale your business

ChatGPT serves as a versatile tool for a wide range of tasks, leveraging its natural language processing capabilities to enhance productivity and streamline various processes. Users can harness its power to save time, improve content quality, and make sense of complex information.



September 1, 2023

ChatGPT has become popular, changing the way people work and what they may find online. Many people are intrigued by the potential of AI chatbots, even those who haven’t tried them. Cybercriminals are looking for ways to profit from this trend.

Netenrich researchers have discovered a new artificial intelligence tool called “FraudGPT.” This AI bot was created specifically for malicious activities, such as sending spear phishing emails, developing cracking tools, and doing carding. It is available for purchase on several Dark Web marketplaces and the Telegram app.

FraudGPT: The dark evolution of ChatGPT into an AI weapon for cybercriminals in 2023 | Data Science Dojo

What is FraudGPT?

FraudGPT is similar to ChatGPT, but it can also generate content for use in cyberattacks. It was first advertised by Netenrich threat researchers in July 2023. One of FraudGPT’s selling points is that it does not have the safeguards and restrictions that make ChatGPT unresponsive to questionable queries.

According to the information provided, the tool is updated every week or two and uses several different types of artificial intelligence. FraudGPT is primarily subscription-based, with monthly subscriptions costing $200 and annual memberships costing $1,700.

How does FraudGPT work?

Netenrich researchers purchased and tested FraudGPT. The layout is very similar to ChatGPT’s, with a history of the user’s requests in the left sidebar and the chat window taking up most of the screen real estate. To get a response, users simply need to type their question into the box provided and hit “Enter.”

One of the test cases for the tool was a phishing email related to a bank. The user input was minimal; simply including the bank’s name in the query format was all that was required for FraudGPT to complete its task. It even indicated where a malicious link could be placed in the text. Scam landing pages that actively solicit personal information from visitors are also within FraudGPT’s capabilities.

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FraudGPT was also asked to name the most frequently visited or exploited online resources. This information could be useful for hackers to use in planning future attacks. An online ad for the software boasted that it could generate harmful code to assemble undetectable malware to search for vulnerabilities and identify targets.

The Netenrich team also discovered that the seller of FraudGPT had previously advertised hacking services for hire. They also linked the same person to a similar program called WormGPT.

The FraudGPT investigation highlights the importance of vigilance.

It is still unknown whether hackers have already used these technologies to develop new threats. However, FraudGPT and similar malicious programs could help hackers save time by creating phishing emails and landing pages in seconds.

Therefore, consumers should be wary of any requests for their personal information and follow other cybersecurity best practices. Cybersecurity professionals would be wise to keep their threat-detection tools up to date, as malicious actors may use programs like FraudGPT to target and enter critical computer networks directly.

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The analysis of FraudGPT is a sobering reminder that hackers will continue to adapt their methods over time. However, open-source software also has security flaws. Anyone who uses the internet or is responsible for securing online infrastructure must stay up-to-date on emerging technologies and the threats they pose. The key is to be aware of the risks involved when using programs like ChatGPT.

Tips for enhancing cybersecurity amid the rise of FraudGPT

The examination of FraudGPT underscores the importance of maintaining a vigilant stance. Given the novelty of these tools, it remains uncertain when hackers might leverage them to concoct previously unseen threats, or if they have already done so. Nevertheless, FraudGPT and comparable products designed for malevolent purposes could significantly expedite hackers’ activities, enabling them to compose phishing emails or craft entire landing pages within seconds.

As a result, it is imperative for individuals to persist in adhering to cybersecurity best practices, which encompass perpetually harboring suspicion towards requests for personal data. Professionals in the cybersecurity domain should ensure their threat-detection utilities are up to date, recognizing that malicious actors may deploy tools like FraudGPT to directly target and infiltrate online infrastructures.

Beyond hackers: Other threats abound

The integration of ChatGPT into more job roles may not bode well for cybersecurity. Employees could inadvertently jeopardize sensitive corporate information by copying and pasting it into ChatGPT. Notably, several companies, including Apple and Samsung, have already imposed limitations on how employees can utilize this tool within their respective roles.

One study has indicated that a staggering 72% of small businesses fold within two years of data loss. Often, individuals only associate criminal activity with the loss of information. However, forward-thinking individuals recognize the inherent risk associated with pasting confidential or proprietary data into ChatGPT.