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AI video generators are tools leveraging artificial intelligence to automate and enhance various stages of the video production process, from ideation to post-production. These generators are transforming the industry by providing new capabilities for creators, allowing them to turn text into videos, add animations, and create realistic avatars and scenes using AI algorithms.

An example of an AI video generator is Synthesia, which enables users to produce videos from uploaded scripts read by AI avatars. Synthesia is used for creating educational content and other types of videos, which was once a long, multi-staged process that’s now been condensed into using a single piece of software.

Additionally, platforms like InVideo are utilized to quickly repurpose blog content into videos and create video scripts, significantly aiding marketers by simplifying the video ad creation process.

 

Read more about: Effective strategies of prompt engineering

 

These AI video generators not only improve the efficiency of video production but also enhance the quality and creativity of the output. Runway ML is one such tool that offers a suite of AI-powered video editing features, allowing filmmakers to seamlessly remove objects or backgrounds and automate tasks that would otherwise take significant time and expertise .

 

 

 

7 Prompting techniques to generate AI videos

Certainly! Here are some techniques for prompting AI video generators to produce the most relevant video content:

 

prompting for AI video generator
Prompting techniques to use AI video generators

 

 

  1. Define clear objectives: Specify exactly what you want the video to achieve. For instance, if the video is for a product launch, outline the key features, use cases, and desired customer reactions to guide the AI’s content creation.
  2. Detailed Script Prompts: Provide not just the script but also instructions regarding voice, tone, and the intended length of the video. Make sure to communicate the campaign goals and the target audience to align the AI-generated video with your strategy.
  3. Visual Descriptions: When aiming for a specific visual style, such as storyboarding or art direction, include detailed descriptions of the desired imagery, color schemes, and overall aesthetic. Art directors, for instance, use AI tools to explore and visualize concepts effectively.
  4. Storyboarding Assistance: Use AI to transform descriptive text into visual storyboards. For example, Arturo Tedeschi utilized DALL-E to convert text from classic movies into visual storyboards, capturing the link between language and images.
  5. Shot List Generation: Turn a script into a detailed shot list by using AI tools, ensuring to capture the desired flow within the specified timeframe.
  6. Feedback Implementation: Iterate on previously generated images to refine the visual style. Midjourney and other similar AI text-to-image generators allow for the iteration process, making it easy to fine-tune the outcome.
  7. Creative Experimentation: Embrace AI’s unique ‘natural aesthetic’ as cited by filmmakers like Paul Trillo, and experiment with the new visual styles created by AI as they go mainstream.

 

By employing these techniques and providing specific, detailed prompts, you can guide AI video generators to create content that is closer to your desired outcome. Remember that AI tools are powerful but still require human guidance to ensure the resulting videos meet your objectives and creative vision.

 

Read about: 10 steps to become a prompt engineer

 

Prompting method
Prompting method:  Source

 

Prompt examples to generate AI videos

Certainly! Here are some examples of prompts that can be used with AI video generation tools:

Prompt for a product launch video:
“We want to create a product launch video to showcase the features, use cases, and initial customer reactions and encourage viewers to sign up to receive a sample product. The product is [describe your product here]. Please map out a script for the voiceover and a shot list for a 30-second video, along with suggestions for music, transitions, and lighting.” 1

Prompt for transforming written content to video format:
“Please transform this written interview into a case study video format with shot suggestions, intro copy, and a call to action at the end to read the whole case study.” 1

Prompt for an AI-generated call sheet:
“Take all characters from the pages of this script and organize them into a call sheet with character, actor name, time needed, scenes to be rehearsed, schedule, and location.”

Art direction ideation prompt:
“Explore art direction concepts for our next video project, focusing on different color schemes and environmental depth to bring a ‘lively city at night’ theme to the forefront. Provide a selection of visuals that can later be refined.”

AI storyboarding prompt using classic film descriptions:
“Use DALL-E to transform the descriptive text from iconic movie scenes into visual storyboards, emphasizing the interplay between dialogue and imagery that creates a bridge between the screenplay and film.”

These examples of AI video generation prompts provide a clear and structured format for the desired outcome of the video content being produced. When using these prompts with an AI video tool, it’s crucial to specify as many relevant details as possible to achieve the most accurate and satisfying results.

 

Quick prompting test for you

 

 

Here is an interesting read: Advanced prompt engineering to leverage generative AI

 

Impact of AI video generators on Art industry

Automation of Creative Processes: AI video generators automate various creative tasks in video production, such as creating storyboards, concept visualization, and even generating new visual effects, thereby enhancing creative workflows and reducing time spent on manual tasks 2.

Expediting Idea Generation: By using AI tools like ChatGPT, creative teams can brainstorm and visualize ideas more quickly, allowing for faster development of video content concepts and scripts, and supporting a rapid ideation phase in the art industry .

Improvement in Efficiency: AI has made it possible to handle art direction tasks more efficiently, saving valuable time that can be redirected towards other creative endeavors within the art and film industry .

Enhanced Visual Storytelling: Artists like Arturo Tedeschi utilize AI to transform text descriptions from classical movies into visual storyboards, emphasizing the role of AI as a creative bridge in visual storytelling .

Democratizing the Art Industry: AI lowers the barriers to entry for video creation by simplifying complex tasks, enabling a wider range of creators to produce art and enter the filmmaking space, regardless of previous experience or availability of expensive equipment 12.

New Aesthetic Possibilities: Filmmakers like Paul Trillo embrace the unique visual style that AI video generators create, exploring these new aesthetics to expand the visual language within the art industry .

Redefining Roles in Art Production: AI is shifting the focus of artists and production staff by reducing the need for certain traditional skills, enabling them to focus on more high-value, creative work instead 2.

Consistency and Quality in Post-Production: AI aids in maintaining a consistent and professional look in post-production tasks like color grading and sound design, contributing to the overall quality output in art and film production.

Innovation in Special Effects: AI tools like Gen-1 apply video effects to create new videos in different styles, advancing the capabilities for special effects and visual innovation significantly.

Supporting Sound Design: AI in the art industry improves audio elements by syncing sounds and effects accurately, enhancing the auditory experience of video artworks.

Facilitating Art Education: AI tools are being implemented in building multimedia educational tools for art, such as at Forecast Academy, which features AI-generated educational videos, enabling more accessible art education.

Optimization of Pre-production Tasks: AI enhances the pre-production phase by optimizing tasks such as scheduling and logistics, which is integral for art projects with large-scale production needs.

The impacts highlighted above demonstrate the multifaceted ways AI video generators are innovating in the art and film sectors, driving forward a new era of creativity and efficiency.

 

Learn to build LLM applications

 

 

Emerging visual styles and aesthetics

One emerging visual style as AI video tools become mainstream is the “natural aesthetic” that the AI videos are creating, particularly appreciated by filmmakers such as Paul Trillo. He acknowledges the distinct visual style born out of AI’s idiosyncrasies and chooses to lean into it rather than resist, finding it intriguing as its own aesthetic.

 

Image generated using AI

 

Tools like Runway ML offer capabilities that can transform video footage drastically, providing cheaper and more efficient ways to create unique visual effects and styles. These AI tools enable new expressions in stylized footage and the crafting of scenes that might have been impossible or impractical before.

AI is also facilitating the creation of AI-generated music videos, visual effects, and even brand-new forms of content that are changing the audience’s viewing experience. This includes AI’s ability to create photorealistic backgrounds and personalized video content, thus diversifying the palette of visual storytelling.

Furthermore, AI tools can emulate popular styles, such as the Wes Anderson color grading effect, by applying these styles to videos automatically. This creates a range of styles quickly and effortlessly, encouraging a trend where even brands like Paramount Pictures follow suit.
In summary, AI video tools are introducing an assortment of new visual styles and aesthetics that are shaping a new mainstream visual culture, characterized by innovative effects, personalized content, and efficient emulation of existing styles.

 

Future of AI video video generators

The revolutionary abilities of these AI video generators promise a future landscape of filmmaking where both professionals and amateurs can produce content at unprecedented speed, with a high degree of customization and lower costs. The adoption of such tools suggests a positive outlook for the democratization of video production, with AI serving as a complement to human creativity rather than a replacement.

Moreover, the integration of AI tools like Adobe’s Firefly into established software such as Adobe After Effects enables the automation of time-consuming manual tasks, leading to faster pre-production, production, and post-production workflows. This allows creators to focus more on the creative aspects of filmmaking and less on the technical grunt work.

February 24, 2024

Prompt engineering is the process of designing and refining prompts that are given to large language models (LLMs) to get them to generate the desired output.

The beginning of prompt engineering

The history of prompt engineering can be traced back to the early days of artificial intelligence when researchers were experimenting with ways to get computers to understand and respond to natural language.

Learn in detail about —> Prompt Engineering

Best practices for prompt engineering
Best practices for prompt engineering

One of the earliest examples of prompt engineering was the work of Terry Winograd in the 1970s. Winograd developed a system called SHRDLU that could answer questions about a simple block world. SHRDLU was able to do this by using a set of prompts that were designed to help it understand the context of the question.

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In the 1980s, prompt engineering became more sophisticated as researchers developed new techniques for training LLMs. One of the most important techniques was backpropagation, which allowed Large Language Models to learn from their mistakes. This made it possible to train LLMs on much larger datasets, leading to significant performance improvements.

In the 2010s, the development of deep learning led to a new wave of progress in prompt engineering. Deep learning models are able to learn much more complex relationships between words than previous models. This has made it possible to create prompts that are much more effective at controlling the output of LLMs.

Today, prompt engineering is a critical tool for researchers and developers who are working with LLMs. It is used in a wide variety of applications, including machine translation, text summarization, and creative writing.

Myths vs facts in prompt engineering

Have you tried any of these fun prompts?

  • In the field of machine translation, one researcher tried to get an LLM to translate the phrase “I am a large language model” into French. The LLM responded with “Je suis un grand modèle linguistique”, which is a grammatically correct translation, but it also happens to be the name of a popular French cheese.
  • In the field of text summarization, one researcher tried to get an LLM to summarize the plot of the movie “The Shawshank Redemption”. The LLM responded with a summary that was surprisingly accurate, but it also included a number of jokes and puns.
  • In the field of creative writing, one researcher tried to get an LLM to write a poem about a cat. The LLM responded with a poem that was both funny and touching.

These are just a few examples of the many funny prompts that people have tried with LLMs. As LLMs become more powerful, it is likely that we will see even more creative and entertaining uses of prompt engineering.

Want to improve your prompting skills? Click below:

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Some unknown facts about Prompt Engineering

  • It is a relatively new field, and there is still much that we do not know about it. However, it is a rapidly growing field, and there are many exciting new developments happening all the time.
  • The effectiveness of a prompt can depend on a number of factors, including the specific LLM being used, the training data that the LLM has been trained in, and the context in which the prompt is being used.
  • There are a number of different techniques that can be used for prompt engineering, and the best technique to use will depend on the specific application.
  • It can be used to control a wide variety of aspects of the output of an LLM, including the length, style, and content of the output.
  • It can be used to generate creative and interesting text, as well as to solve complex problems.
  • It is a powerful tool that can be used to unlock the full potential of LLMs.

 

Learn how to become a prompt engineer in 10 steps 

10 steps to become a prompt engineer
10 steps to become a prompt engineer

Here are some specific examples of important and unknown facts about prompting:

  • It is possible to use prompts to control the creativity of an LLM. For example, one study found that adding the phrase “in a creative way” to a prompt led to more creative outputs from the LLM.
  • Prompts can be used to generate text that is consistent with a particular style. For example, one study found that adding the phrase “in the style of Shakespeare” to a prompt led to outputs that were more Shakespearean in style.
  • Prompts can be used to solve complex problems. For example, one study found that adding the phrase “prove that” to a prompt led to the LLM generating mathematical proofs.
  • It is a complex and challenging task. There is no one-size-fits-all approach to prompt engineering, and the best way to create effective prompts will vary depending on the specific application.
  • It is a rapidly evolving field. There are new developments happening all the time, and the field is constantly growing and changing.

Most popular myths and facts of prompt engineering

In this ever-evolving realm, it’s crucial to discern fact from fiction to stay ahead of the curve. Our team of experts has meticulously sifted through the noise to present you with the most accurate insights, dispelling myths that might have clouded your understanding. Let’s delve into the heart of prompting and uncover the truths that can drive your success.

Myth: Prompt engineering is just about keywords

Fact: Prompt engineering is a symphony of elements

Gone are the days when prompt engineering was solely about sprinkling keywords like confetti. Today, it’s a meticulous symphony of various components working harmoniously. While keywords remain pivotal, they’re just one part of the grand orchestra. Structured data, user intent analysis, and contextual relevance are the unsung heroes that make your prompt engineering soar. Balancing these elements crafts a narrative that resonates with both users and search engines.

Myth: More prompts, higher results

Fact: Quality over quantity

Quantity might impress at first glance, but it’s quality that truly wields power in the world of prompt engineering. Crafting a handful of compelling, highly relevant prompts that align seamlessly with your content yields far superior results than flooding your page with irrelevant ones. Remember, it’s the value you provide that keeps users engaged, not the sheer number of prompts you throw their way.

Myth: Prompt engineering is a one-time task

Fact: Ongoing optimization is the key

Imagine your website as a garden that requires constant tending. Similarly, prompt engineering demands continuous attention. Regularly analyzing the performance of your prompts and adapting to shifting trends is paramount. This ensures that your content remains evergreen and resonates with the dynamic preferences of your audience.

Myth: Creativity has no place in prompt engineering

Fact: Creativity elevates engagement

While prompt engineering involves a systematic approach, creativity is the secret ingredient that adds flavor to the mix. Crafting prompts that spark curiosity, evoke emotion, or present a unique perspective can exponentially boost user engagement. Metaphors, analogies, and storytelling are potent tools that, when woven into your prompts, make your content unforgettable.

Myth: Only text prompts matter

Fact: Diversify with various formats

Text prompts are undeniably significant, but limiting yourself to them is a missed opportunity. Embrace a diverse range of prompt formats to cater to different learning styles and preferences.

Visual prompts, such as infographics and videos, engage visual learners, while audio prompts cater to those who prefer auditory learning. The more versatile your prompt formats, the broader your audience reaches.

Myth: Prompt engineering and SEO are unrelated

Fact: Symbiotic relationship

Prompt engineering and SEO are not isolated islands; they’re interconnected domains that thrive on collaboration. Solid prompt engineering bolsters SEO by providing search engines with the context they crave. Conversely, a well-optimized website enhances prompt engineering, as it ensures your content is easily discoverable by your target audience.

Myth: Complex language boosts credibility

Fact: Clarity trumps complexity

Using complex jargon might seem like a credibility booster, but it often does more harm than good. Clear, concise prompts that resonate with a broader audience hold more weight. Remember, the goal is not to showcase your vocabulary prowess but to communicate effectively and establish a genuine connection with your readers.

Myth: Prompt engineering is set-and-forget

Fact: Continuous monitoring is vital

Once you’ve orchestrated your prompts, it’s not time to sit back and relax. The digital landscape is in perpetual motion, and so should be your approach to prompt engineering. Monitor the performance of your prompts regularly, employing data analytics to identify patterns and make informed adjustments that keep your content relevant and engaging.

Myth: Only experts can master prompt engineering

Fact: Learning and iteration lead to mastery

While prompt engineering might appear daunting, it’s a skill that can be honed with dedication and a willingness to learn. Don’t shy away from experimentation and iteration. Embrace the insights gained from your data, be open to refining your approach, and gradually you’ll find yourself mastering the art of prompt engineering.

Get on the journey of prompt engineering

Prompt engineering is a dynamic discipline that demands both strategy and creativity. Dispelling these myths and embracing the facts will propel your content to new heights, setting you apart from the competition. Remember, prompt engineering is not a one-size-fits-all solution; it’s an evolving journey of discovery that, when approached with dedication and insight, can yield remarkable results

August 21, 2023

In today’s era of advanced artificial intelligence, language models like OpenAI’s GPT-3.5 have captured the world’s attention with their astonishing ability to generate human-like text. However, to harness the true potential of these models, it is crucial to master the art of prompt engineering.



How to curate a good prompt?

A well-crafted prompt holds the key to unlocking accurate, relevant, and insightful responses from language models. In this blog post, we will explore the top characteristics of a good prompt and discuss why everyone should learn prompt engineering. We will also delve into the question of whether prompt engineering might emerge as a dedicated role in the future.

Best practices for prompt engineering
Best practices for prompt engineering – Data Science Dojo

 

Prompt engineering refers to the process of designing and refining input prompts for AI language models to produce desired outputs. It involves carefully crafting the words, phrases, symbols, and formats used as input to guide the model in generating accurate and relevant responses. The goal of prompt engineering is to improve the performance and output quality of the language model.

 

Here’s a simple example to illustrate prompt engineering:

Imagine you are using a chatbot AI model to provide information about the weather. Instead of a generic prompt like “What’s the weather like?”, prompt engineering involves crafting a more specific and detailed prompt like “What is the current temperature in New York City?” or “Will it rain in London tomorrow?”

 

Read about —> Which AI chatbot is right for you in 2023

 

By providing a clear and specific prompt, you guide the AI model to generate a response that directly answers your question. The choice of words, context, and additional details in the prompt can influence the output of the AI model and ensure it produces accurate and relevant information.

Quick exercise –> Choose the most suitable prompt

 

Prompt engineering is crucial because it helps optimize the performance of AI models by tailoring the input prompts to the desired outcomes. It requires creativity, understanding of the language model, and attention to detail to strike the right balance between specificity and relevance in the prompts.

Different resources provide guidance on best practices and techniques for prompt engineering, considering factors like prompt formats, context, length, style, and desired output. Some platforms, such as OpenAI API, offer specific recommendations and examples for effective prompt engineering.

 

Why everyone should learn prompt engineering:

 

Prompt engineering - Marketoonist
Prompt Engineering | Credits: Marketoonist

 

1. Empowering communication: Effective communication is at the heart of every interaction. By mastering prompt engineering, individuals can enhance their ability to extract precise and informative responses from language models. Whether you are a student, professional, researcher, or simply someone seeking knowledge, prompt engineering equips you with a valuable tool to engage with AI systems more effectively.

2. Tailored and relevant information: A well-designed prompt allows you to guide the language model towards providing tailored and relevant information. By incorporating specific details and instructions, you can ensure that the generated responses align with your desired goals. Prompt engineering enables you to extract the exact information you seek, saving time and effort in sifting through irrelevant or inaccurate results.

3. Enhancing critical thinking: Crafting prompts demand careful consideration of context, clarity, and open-endedness. Engaging in prompt engineering exercises cultivates critical thinking skills by challenging individuals to think deeply about the subject matter, formulate precise questions, and explore different facets of a topic. It encourages creativity and fosters a deeper understanding of the underlying concepts.

4. Overcoming bias: Bias is a critical concern in AI systems. By learning prompt engineering, individuals can contribute to reducing bias in generated responses. Crafting neutral and unbiased prompts helps prevent the introduction of subjective or prejudiced language, resulting in more objective and balanced outcomes.

 

Top characteristics of a good prompt with examples

Prompting example
An example of a good prompt – Credits Gridfiti

 

 

A good prompt possesses several key characteristics that can enhance the effectiveness and quality of the responses generated. Here are the top characteristics of a good prompt:

1. Clarity:

A good prompt should be clear and concise, ensuring that the desired question or topic is easily understood. Ambiguous or vague prompts can lead to confusion and produce irrelevant or inaccurate responses.

Example:

Good Prompt: “Explain the various ways in which climate change affects the environment.”

Poor Prompt: “Climate change and the environment.”

2. Specificity:

Providing specific details or instructions in a prompt help focus the generated response. By specifying the context, parameters, or desired outcome, you can guide the language model to produce more relevant and tailored answers.

Example:

Good Prompt: “Provide three examples of how rising temperatures due to climate change impact marine ecosystems.”
Poor Prompt: “Talk about climate change.”

3. Context:

Including relevant background information or context in the prompt helps the language model understand the specific domain or subject matter. Contextual cues can improve the accuracy and depth of the generated response.

Example: 

Good Prompt: “In the context of agricultural practices, discuss how climate change affects crop yields.”

Poor Prompt: “Climate change effects

4. Open-endedness:

While specificity is important, an excessively narrow prompt may limit the creativity and breadth of the generated response. Allowing room for interpretation and open-ended exploration can lead to more interesting and diverse answers.

Example:

Good Prompt: “Describe the short-term and long-term consequences of climate change on global biodiversity.”

Poor Prompt: “List the effects of climate change.”

 

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5. Conciseness:

Keeping the prompt concise helps ensure that the language model understands the essential elements and avoids unnecessary distractions. Lengthy or convoluted prompts might confuse the model and result in less coherent or relevant responses.

Example:
Good Prompt: “Summarize the key impacts of climate change on coastal communities.”

Poor Prompt: “Please explain the negative effects of climate change on the environment and people living near the coast.”

6. Correct grammar and syntax:

A well-structured prompt with proper grammar and syntax is easier for the language model to interpret accurately. It reduces ambiguity and improves the chances of generating coherent and well-formed responses.

Example:

Good Prompt: “Write a paragraph explaining the relationship between climate change and species extinction.”
Poor Prompt: “How species extinction climate change.”

7. Balanced complexity:

The complexity of the prompt should be appropriate for the intended task or the model’s capabilities. Extremely complex prompts may overwhelm the model, while overly simplistic prompts may not challenge it enough to produce insightful or valuable responses.

Example:

Good Prompt: “Discuss the interplay between climate change, extreme weather events, and natural disasters.”

Poor Prompt: “Climate change and weather.”

8. Diversity in phrasing:

When exploring a topic or generating multiple responses, varying the phrasing or wording of the prompt can yield diverse perspectives and insights. This prevents the model from repeating similar answers and encourages creative thinking.

Example:

Good Prompt: “How does climate change influence freshwater availability?” vs. “Explain the connection between climate change and water scarcity.”

Poor Prompt: “Climate change and water.

9. Avoiding leading or biased language:

To promote neutrality and unbiased responses, it’s important to avoid leading or biased language in the prompt. Using neutral and objective wording allows the language model to generate more impartial and balanced answers.

Example:

Good Prompt: “What are the potential environmental consequences of climate change?”

Poor Prompt: “How does climate change devastate the environment?”

10. Iterative refinement:

Crafting a good prompt often involves an iterative process. Reviewing and refining the prompt based on the generated responses can help identify areas of improvement, clarify instructions, or address any shortcomings in the initial prompt.

Example:

Prompt iteration involves an ongoing process of improvement based on previous responses and refining the prompts accordingly. Therefore, there is no specific example to provide, as it is a continuous effort.

By considering these characteristics, you can create prompts that elicit meaningful, accurate, and relevant responses from the language model.

 

Read about —-> How LLMs (Large Language Models) technology is making chatbots smarter in 2023?

 

Two different approaches of prompting

Prompting by instruction and prompting by example are two different approaches to guide AI language models in generating desired outputs. Here’s a detailed comparison of both approaches, including reasons and situations where each approach is suitable:

1. Prompting by instruction:

  • In this approach, the prompt includes explicit instructions or explicit questions that guide the AI model on how to generate the desired output.
  • It is useful when you need specific control over the generated response or when you want the model to follow a specific format or structure.
  • For example, if you want the AI model to summarize a piece of text, you can provide an explicit instruction like “Summarize the following article in three sentences.”
  • Prompting by instruction is suitable when you need a precise and specific response that adheres to a particular requirement or when you want to enforce a specific behavior in the model.
  • It provides clear guidance to the model and allows you to specify the desired outcome, length, format, style, and other specific requirements.

 

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Examples of prompting by instruction:

  1. In a classroom setting, a teacher gives explicit verbal instructions to students on how to approach a new task or situation, such as explaining the steps to solve a math problem.
  2. In Applied Behavior Analysis (ABA), a therapist provides a partial physical prompt by using their hands to guide a student’s behavior in the right direction when teaching a new skill.
  3. When using AI language models, an explicit instruction prompt can be given to guide the model’s behavior. For example, providing the instruction “Summarize the following article in three sentences” to prompt the model to generate a concise summary.

 

Tips for prompting by instruction:

    • Put the instructions at the beginning of the prompt and use clear markers like “A:” to separate instructions and context.
    • Be specific, descriptive, and detailed about the desired context, outcome, format, style, etc.
    • Articulate the desired output format through examples, providing clear guidelines for the model to follow.

 

2. Prompting by example:

  • In this approach, the prompt includes examples of the desired output or similar responses that guide the AI model to generate responses based on those examples.
  • It is useful when you want the model to learn from specific examples and mimic the desired behavior.
  • For example, if you want the AI model to answer questions about a specific topic, you can provide example questions and their corresponding answers.
  • Prompting by example is suitable when you want the model to generate responses similar to the provided examples or when you want to capture the style, tone, or specific patterns from the examples.
  • It allows the model to learn from the given examples and generalize its behavior based on them.

 

Examples of prompting by example:

  1. In a classroom, a teacher shows students a model essay as an example of how to structure and write their own essays, allowing them to learn from the demonstrated example.
  2. In AI language models, providing example questions and their corresponding answers can guide the model in generating responses similar to the provided examples. This helps the model learn the desired behavior and generalize it to new questions.
  3. In an online learning environment, an instructor provides instructional prompts in response to students’ discussion forum posts, guiding the discussion and encouraging deep understanding. These prompts serve as examples for the entire class to enhance the learning experience.

 

Tips for prompting by example:

    • Provide a variety of examples to capture different aspects of the desired behavior.
    • Include both positive and negative examples to guide the model on what to do and what not to do.
    • Gradually refine the examples based on the model’s responses, iteratively improving the desired behavior.

 

Which prompting approach is right for you?

Prompting by instruction provides explicit guidance and control over the model’s behavior, while prompting by example allows the model to learn from provided examples and mimic the desired behavior. The choice between the two approaches depends on the level of control and specificity required for the task at hand. It’s also possible to combine both approaches in a single prompt to leverage the benefits of each approach for different parts of the task or desired behavior.

To become proficient in prompt engineering, register now in our upcoming Large Language Models Bootcamp

July 12, 2023

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