Ayesha Aamir

Graph rag is rapidly emerging as the gold standard for context-aware AI, transforming how large language models (LLMs) interact with knowledge. In this comprehensive guide, we’ll explore the technical foundations, architectures, use cases, and best practices of graph rag versus traditional RAG, helping you understand which approach is best for your enterprise AI, research, or product development needs.

Why Graph RAG Matters

Graph rag sits at the intersection of retrieval-augmented generation, knowledge graph engineering, and advanced context engineering. As organizations demand more accurate, explainable, and context-rich AI, graph rag is becoming essential for powering next-generation enterprise AI, agentic AI, and multi-hop reasoning systems.

Traditional RAG systems have revolutionized how LLMs access external knowledge, but they often fall short when queries require understanding relationships, context, or reasoning across multiple data points. Graph rag addresses these limitations by leveraging knowledge graphs—structured networks of entities and relationships—enabling LLMs to reason, traverse, and synthesize information in ways that mimic human cognition.

For organizations and professionals seeking to build robust, production-grade AI, understanding the nuances of graph rag is crucial. Data Science Dojo’s LLM Bootcamp and Agentic AI resources are excellent starting points for mastering these concepts.

What is Retrieval-Augmented Generation (RAG)?

Retrieval-augmented generation (RAG) is a foundational technique in modern AI, especially for LLMs. It bridges the gap between static model knowledge and dynamic, up-to-date information by retrieving relevant data from external sources at inference time.

How RAG Works

1. Indexing: Documents are chunked and embedded into a vector database.
2. Retrieval: At query time, the system finds the most semantically relevant chunks using vector similarity search.
3. Augmentation: Retrieved context is concatenated with the user’s prompt and fed to the LLM.
4. Generation: The LLM produces a grounded, context-aware response.

Benefits of RAG:

• Reduces hallucinations
• Enables up-to-date, domain-specific answers
• Provides source attribution
• Scales to enterprise knowledge needs

For a hands-on walkthrough, see RAG in LLM – Elevate Your Large Language Models Experience and What is Context Engineering?.

What is Graph RAG?

Graph rag is an advanced evolution of RAG that leverages knowledge graphs—structured representations of entities (nodes) and their relationships (edges). Instead of retrieving isolated text chunks, graph rag retrieves interconnected entities and their relationships, enabling multi-hop reasoning and deeper contextual understanding.

Key Features of Graph RAG

• Multi-hop Reasoning: Answers complex queries by traversing relationships across multiple entities.
• Contextual Depth: Retrieves not just facts, but the relationships and context connecting them.
• Structured Data Integration: Ideal for enterprise data, scientific research, and compliance scenarios.
• Explainability: Provides transparent reasoning paths, improving trust and auditability.

Learn more about advanced RAG techniques in the Large Language Models Bootcamp.

Technical Architecture: RAG vs Graph RAG

Traditional RAG Pipeline

• Vector Database: Stores embeddings of text chunks.
• Retriever: Finds top-k relevant chunks for a query using vector similarity.
• LLM: Generates a response using retrieved context.

Limitations:

Traditional RAG is limited to single-hop retrieval and struggles with queries that require understanding relationships or synthesizing information across multiple documents.

Graph RAG Pipeline

• Knowledge Graph: Stores entities and their relationships as nodes and edges.
• Graph Retriever: Traverses the graph to find relevant nodes, paths, and multi-hop connections.
• LLM: Synthesizes a response using both entities and their relationships, often providing reasoning chains.

Why Graph RAG Excels:

Graph rag enables LLMs to answer questions that require understanding of how concepts are connected, not just what is written in isolated paragraphs. For example, in healthcare, graph rag can connect symptoms, treatments, and patient history for more accurate recommendations.

For a technical deep dive, see Mastering LangChain and Retrieval Augmented Generation.

Key Differences and Comparative Analysis

Use Cases: When to Use RAG vs Graph RAG

Traditional RAG

• Customer support chatbots
• FAQ answering
• Document summarization
• News aggregation
• Simple enterprise search

Graph RAG

• Enterprise AI: Unified search across siloed databases, CRMs, and wikis.
• Healthcare: Multi-hop reasoning over patient data, treatments, and research.
• Finance: Compliance checks by tracing relationships between transactions and regulations.
• Scientific Research: Discovering connections between genes, diseases, and drugs.
• Personalization: Hyper-personalized recommendations by mapping user preferences to product graphs.

Explore more enterprise applications in Data and Analytics Services.

Case Studies: Real-World Impact

Case Study 1: Healthcare Knowledge Assistant

A leading hospital implemented graph rag to power its clinical decision support system. By integrating patient records, drug databases, and medical literature into a knowledge graph, the assistant could answer complex queries such as:

• “What is the recommended treatment for a diabetic patient with hypertension and a history of kidney disease?”

Impact:

• Reduced diagnostic errors by 30%
• Improved clinician trust due to transparent reasoning paths

Case Study 2: Financial Compliance

A global bank used graph rag to automate compliance checks. The system mapped transactions, regulations, and customer profiles in a knowledge graph, enabling multi-hop queries like:

• “Which transactions are indirectly linked to sanctioned entities through intermediaries?”

Impact:

• Detected 2x more suspicious patterns than traditional RAG
• Streamlined audit trails for regulatory reporting

Case Study 3: Data Science Dojo’s LLM Bootcamp

Participants in the LLM Bootcamp built both RAG and graph rag pipelines. They observed that graph rag consistently outperformed RAG in tasks requiring reasoning across multiple data sources, such as legal document analysis and scientific literature review.

Best Practices for Implementation

1. Start with RAG:

   Use traditional RAG for unstructured data and simple Q&A.

2. Adopt Graph RAG for Complexity:

   When queries require multi-hop reasoning or relationship mapping, transition to graph rag.

3. Leverage Hybrid Approaches:

   Combine vector search and graph traversal for maximum coverage.

4. Monitor and Benchmark:

   Use hybrid scorecards to track both AI quality and engineering velocity.

5. Iterate Relentlessly:

   Experiment with chunking, retrieval, and prompt formats for optimal results.

6. Treat Context as a Product:

   Apply version control, quality checks, and continuous improvement to your context pipelines.

7. Structure Prompts Clearly:

   Separate instructions, context, and queries for clarity.

8. Leverage In-Context Learning:

   Provide high-quality examples in the prompt.

9. Security and Compliance:

   Guard against prompt injection, data leakage, and unauthorized tool use.

10. Ethics and Privacy:

    Ensure responsible use of interconnected personal or proprietary data.

For more, see What is Context Engineering?

Challenges, Limitations, and Future Trends

Challenges

• Context Quality Paradox: More context isn’t always better—balance breadth and relevance.
• Scalability: Graph rag can be resource-intensive; optimize graph size and traversal algorithms.
• Security: Guard against data leakage and unauthorized access to sensitive relationships.
• Ethics and Privacy: Ensure responsible use of interconnected personal or proprietary data.
• Performance: Graph traversal can introduce latency compared to vector search.

Future Trends

• Context-as-a-Service: Platforms offering dynamic context assembly and delivery.
• Multimodal Context: Integrating text, audio, video, and structured data.
• Agentic AI: Embedding graph rag in multi-step agent loops with planning, tool use, and reflection.
• Automated Knowledge Graph Construction: Using LLMs and data pipelines to build and update knowledge graphs in real time.
• Explainable AI: Graph rag’s reasoning chains will drive transparency and trust in enterprise AI.

Emerging trends include context-as-a-service platforms, multimodal context (text, audio, video), and contextual AI ethics frameworks. For more, see Agentic AI.

Frequently Asked Questions (FAQ)

Q1: What is the main advantage of graph rag over traditional RAG?

A: Graph rag enables multi-hop reasoning and richer, more accurate responses by leveraging relationships between entities, not just isolated facts.

Q2: When should I use graph rag?

A: Use graph rag when your queries require understanding of how concepts are connected—such as in enterprise search, compliance, or scientific discovery.

Q3: What frameworks support graph rag?

A: Popular frameworks include LangChain and LlamaIndex, which offer orchestration, memory management, and integration with vector databases and knowledge graphs.

Q4: How do I get started with RAG and graph rag?

A: Begin with Retrieval Augmented Generation and explore advanced techniques in the LLM Bootcamp.

Q5: Is graph rag slower than traditional RAG?

A: Graph rag can be slower due to graph traversal and reasoning, but it delivers superior accuracy and explainability for complex queries 1.

Q6: Can I combine RAG and graph rag in one system?

A: Yes! Many advanced systems use a hybrid approach, first retrieving relevant documents with RAG, then mapping entities and relationships with graph rag for deeper reasoning.

Conclusion & Next Steps

Graph rag is redefining what’s possible with retrieval-augmented generation. By enabling LLMs to reason over knowledge graphs, organizations can unlock new levels of accuracy, transparency, and insight in their AI systems. Whether you’re building enterprise AI, scientific discovery tools, or next-gen chatbots, understanding the difference between graph rag and traditional RAG is essential for staying ahead.

Ready to build smarter AI?

• Explore Data Science Dojo’s LLM Bootcamp for hands-on training.
• Dive into Mastering LangChain for practical graph rag workflows.
• Stay updated with the latest in Agentic AI.

In the ever-evolving landscape of natural language processing (NLP), embedding techniques have played a pivotal role in enhancing the capabilities of language models.

The birth of Word Embeddings

Before venturing into the large number of embedding techniques that have emerged in the past few years, we must first understand the problem that led to the creation of such techniques.

Word embeddings were created to address the absence of efficient text representations for NLP models. Since NLP techniques operate on textual data, which inherently cannot be directly integrated into machine learning models designed to process numerical inputs, a fundamental question arose: how can we convert text into a format compatible with these models?

Lean more about Text Analytics

 

Basic approaches like one-hot encoding and Bag-of-Words (BoW) were employed in the initial phases of NLP development. However, these methods were eventually discarded due to their evident shortcomings in capturing the contextual and semantic nuances of language. Each word was treated as an isolated unit, without understanding its relationship with other words or its usage in different contexts.

 

 

Word2Vec 

In 2013, Google presented a new technique to overcome the shortcomings of the previous word embedding techniques, called Word2Vec. It represents words in a continuous vector space, better known as an embedding space, where semantically similar words are located close to each other.

This contrasted with traditional methods, like one-hot encoding, which represents words as sparse, high-dimensional vectors. The dense vector representations generated by Word2Vec had several advantages, including the ability to capture semantic relationships, support vector arithmetic (e.g., “king” – “man” + “woman” = “queen”), and improve the performance of various NLP tasks like language modeling, sentiment analysis, and machine translation.

Transition to GloVe and FastText

The success of Word2Vec paved the way for further innovations in the realm of word embeddings. The Global Vectors for Word Representation (GloVe) model, introduced by Stanford researchers in 2014, aimed to leverage global statistical information about word co-occurrences.

GloVe demonstrated improved performance over Word2Vec in capturing semantic relationships. Unlike Word2Vec, GloVe considers the entire corpus when learning word vectors, leading to a more global understanding of word relationships.

Fast forward to 2016, Facebook’s FastText introduced a significant shift by considering sub-word information. Unlike traditional word embeddings, FastText represented words as bags of character n-grams. This sub-word information allowed FastText to capture morphological and semantic relationships in a more detailed manner, especially for languages with rich morphology and complex word formations. This approach was particularly beneficial for handling out-of-vocabulary words and improving the representation of rare words.

The Rise of Transformer Models 

The real game-changer in the evolution of embedding techniques came with the advent of the Transformer architecture. Introduced by researchers at Google in the form of the Attention is All You Need paper in 2017, Transformers demonstrated remarkable efficiency in capturing long-range dependencies in sequences.

The architecture laid the foundation for state-of-the-art models like OpenAI’s GPT (Generative Pre-trained Transformer) series and BERT (Bidirectional Encoder Representations from Transformers). Hence, the traditional understanding of embedding techniques is revamped with new solutions.

 

 

 

Impact of Embedding Techniques on Language Models

The embedding techniques mentioned above have significantly impacted the performance and capabilities of LLMs. Pre-trained models like GPT-3 and BERT leverage these embeddings to understand natural language context, semantics, and syntactic structures. The ability to capture context allows these models to excel in a wide range of NLP tasks, including sentiment analysis, text summarization, and question-answering.

Imagine the sentence: “The movie was not what I expected, but the plot twist at the end made it incredible.”

Traditional models might struggle with the negation of “not what I expected.” Word embeddings could capture some sentiment but might miss the subtle shift in sentiment caused by the positive turn of events in the latter part of the sentence.

In contrast, LLMs with contextualized embeddings can consider the entire sentence and comprehend the nuanced interplay of positive and negative sentiments. They grasp that the initial negativity is later counteracted by the positive twist, resulting in a more accurate sentiment analysis.

Advantages of Embeddings in LLMs

 

 

• Contextual Understanding: LLMs equipped with embeddings comprehend the context in which words appear, allowing for a more nuanced interpretation of sentiment in complex sentences.
• Semantic Relationships: Word embeddings capture semantic relationships between words, enabling the model to understand the subtleties and nuances of language. 
• Handling Ambiguity: Contextual embeddings help LLMs handle ambiguous language constructs, such as negations or sarcasm, contributing to improved accuracy in sentiment analysis.
• Transfer Learning: The pre-training of LLMs with embeddings on vast datasets allows them to generalize well to various downstream tasks, including sentiment analysis, with minimal task-specific data.

To dive even deeper into embeddings and their role in LLMs, click here

How are Enterprises Using Embeddings in their LLM Processes?

In light of recent advancements, enterprises are keen on harnessing the robust capabilities of Large Language Models (LLMs) to construct comprehensive Software as a Service (SAAS) solutions. Nevertheless, LLMs come pre-trained on extensive datasets, and to tailor them to specific use cases, fine-tuning on proprietary data becomes essential.

This process can be laborious. To streamline this intricate task, the widely embraced Retrieval Augmented Generation (RAG) technique comes into play. RAG involves retrieving pertinent information from an external source, transforming it to a format suitable for LLM comprehension, and then inputting it into the LLM to generate textual output.

This innovative approach enables the fine-tuning of LLMs with knowledge beyond their original training scope. In this process, you need an efficient way to store, retrieve, and ingest data into your LLMs to use it accurately for your given use case.

One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding vectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are ‘most similar’ to the embedded query.  Hence, without embedding techniques, your RAG approach will be impossible.

 

 

Understanding the Creation of Embeddings

Much like a machine learning model, an embedding model undergoes training on extensive datasets. Various models available can generate embeddings for you, and each model is distinct. You can find the top embedding models here.

It is unclear what makes an embedding model perform better than others. However, a common way to select one for your use case is to evaluate how many words a model can take in without breaking down. There’s a limit to how many tokens a model can handle at once, so you’ll need to split your data into chunks that fit within the limit. Hence, choosing a suitable model is a good starting point for your use case.

Creating embeddings with Azure OpenAI is a matter of a few lines of code. To create embeddings of a simple sentence like The food was delicious and the waiter…, you can execute the following code blocks:

• First, import AzureOpenAI from OpenAI

 

 

• Load in your environment variables

 

 

• Create your Azure OpenAI client.

 

• Create your embeddings

 

And you’re done! It’s really that simple to generate embeddings for your data. If you want to generate embeddings for an entire dataset, you can follow along with the great notebook provided by OpenAI itself here.

 

 

To Sum It Up!

The evolution of embedding techniques has revolutionized natural language processing, empowering language models with a deeper understanding of context and semantics. From Word2Vec to Transformer models, each advancement has enriched LLM capabilities, enabling them to excel in various NLP tasks.

Enterprises leverage techniques like Retrieval Augmented Generation, facilitated by embeddings, to tailor LLMs for specific use cases. Platforms like Azure OpenAI offer straightforward solutions for generating embeddings, underscoring their importance in NLP development. As we forge ahead, embeddings will remain pivotal in driving innovation and expanding the horizons of language understanding.

 

Data erasure is a software-based process that involves data sanitization or, in plain words, ‘data wiping’ so that no traces of data remain recoverable. This helps with the prevention of data leakage and the protection of sensitive information like trade secrets, intellectual property, or customer information.

 

 

By 2025, it is estimated that data will grow up to 175 Zettabytes, and with great data comes great responsibility. Data plays a pivotal role in both personal and professional lives. May it be confidential records or family photos, data security is important and must always be endorsed.

As the volume of digital information continues to grow, so does the need for safeguarding and securing data. Key data breach statistics show that 21% of all folders in a typical company are open to everyone, leading to malicious attacks, indicating a rise in data leakage and 51% criminal incidents.

 

Understanding Data Erasure

Data erasure is a fundamental practice in the field of data security and privacy. It involves the permanent destruction of data from storage devices like hard disks, solid-state devices, or any other digital media through software or other means.

 

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This practice ensures that data remains completely unrecoverable through any data recovery methods while the device remains reusable (in case software is being used). Data erasure works in regard to an individual person who is disposing of a personal device as well as organizations handling sensitive business information. It guarantees responsible technology disposal.

The science behind data erasure

Data erasure is also known as ‘overwriting’, it involves a process of writing on data with a series of 0s and 1s, making it unreadable and undiscoverable. The overwriting process varies in the number of passes and patterns used.

The type of overwriting depends on multiple factors like the nature of the storage device, the type of data at hand, and the level of security that is needed.

 

 

The ‘number of passes’ refers to the number of times the overwriting process is repeated for a certain storage device. Each pass essentially overwrites the old data with new data. The greater the number of passes, the more thorough the data erasure process is, making it increasingly difficult to recover the demolished data.

‘Patterns’ can make data recovery extremely challenging. This is the reason why different sequences and patterns are written to the data during each pass. In essence, the data erasure process can be customized to cater to different types of scenarios depending upon the sensitivity of the data being erased. Moreover, data erasure is also used to verify whether the erasure process was successful.

 

Read more on how to master data security in warehousing 

The Need for Data Erasure

Confidentiality of business data, prevention of data leakage, and regulation with compliance are some of the reasons we need methods like data erasure especially when someone is relocating, repurposing, or putting a device to rest.

 

 

Traditional methods like data deletion make the data unavailable to the user, but provide the privilege of recovering it through different software.  Likewise, the destruction of physical devices renders the device completely useless.

For this purpose, a software-based erasure method is required. Some crucial factors that drive the need are listed below:

Protection of sensitive information:

Protecting sensitive information from unauthorized access is one of the primary reasons for having data erasure. Data branches or leakage of confidential information like customer information, trade secrets, or proprietary information can lead to severe consequences.

Thus, when the amount of data begins to get unmanageable and enterprises look forward to disposing of a portion of it, it is always advisable to destroy the data in a way that it is not recoverable for misuse later. Proper data erasure techniques help to mitigate the risk associated with cybercrimes.

 

Read more about Data privacy and data anonymization techniques 

 

Data lifecycle management:

Compliance with data protection regulations:

Key Applications of Data Erasure in Key Industries

 

 

Data erasure is vital for businesses handling sensitive information, ensuring secure disposal, regulatory compliance, and protection against data breaches. Below are examples of its implementation across industries:

Corporate IT asset disposal:

When a company decides to retire its previous systems and upgrade to new hardware, it must ensure that any old data that belongs to the company is securely erased from the older devices before they can be sold, donated or recycled.

This prevents sensitive corporate information from falling into the wrong hands. The IT department can use certified data erasure software to securely wipe all sensitive company data, including financial reports, customer databases, and employee records, ensuring that none of this information can be recovered from the devices.

Healthcare data privacy:

Like the corporate industry, Healthcare organisations tend to store confidential patient information in their systems. Hospitals erase patient data, including medical histories and test results, using techniques like cryptographic wiping and degaussing.

 

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If the need arises to upgrade these systems, they must ensure secure data erasure to protect patient confidentiality and to comply with healthcare data privacy regulations. This safeguards privacy and ensures compliance with HIPAA and GDPR, mitigating risks of breaches and identity theft.

Cloud services:

Cloud service providers often have data erasure procedures in place to securely erase customer data from their servers when requested by customers or when the service is terminated.

Cloud providers erase deleted or decommissioned data using logical sanitization, cryptographic erasure, and secure overwriting. Retired servers undergo physical destruction, ensuring no data recovery is possible.

Data center operations:

Data centres often have strict data erasure protocols in place to securely wipe data from hard drives, SSDs, and other storage devices when they are no longer in use. This ensures that customer data is not accessible after the equipment is decommissioned.

Data centers securely erase sensitive data from decommissioned storage devices using multipass overwriting and cryptographic erasure. Compliance with standards like NIST 800-88 ensures secure protocols and protection of client data.

Financial services:

In a situation where a stock brokerage firm needs to retire its older trading servers. These servers would indefinitely contain some form of sensitive financial transaction data and customer account information.

 

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Prior to selling the servers, the firm would have to use hardware-based data erasure solutions to completely overwrite the data and render it irretrievable, ensuring client confidentiality and regulatory compliance.

Safeguard Your Business Data Today!

In the era where data is referred to as the ‘new oil’, safeguarding it has become paramount. Many times, individuals feel hesitant to dispose of their personal devices due to the possible misuse of data present in them.

 

 

The same applies to large organizations, when proper utilization of data has been done, standard measures should be taken to discard the data so that it does not result in unnecessary consequences. To ensure privacy and maintain integrity, data erasure was brought into practice. In an age where data is king, data erasure is the guardian of the digital realm.