Retrieval-Augmented Generation (RAG)

What Is Retrieval-Augmented Generation (RAG)? A Clear Definition

RAG stands for Retrieval-Augmented Generation. It’s a technique that boosts the capabilities of language models by connecting them to real-world data sources (internal databases, policy documents, or cloud-based knowledge bases).

By default, large language models (LLMs) can only respond based on the information they were trained on. That means they don’t know what’s in your company’s documentation or today’s customer support logs, unless you give them access. 

RAG solves that, by pulling in up-to-date, relevant information right when the model needs it.

Best Use Cases for RAG: When Retrieval-Augmented Generation Adds Value

RAG is ideal when:

• Your knowledge base is too large to fit into a single prompt.
  
  
• You need real-time answers from frequently updated sources (e.g., customer support data, CRM entries).
  
  
• You work in domains that require accuracy and traceability, like law, finance, or healthcare.
  
  
• You want the model to reason over private, structured data that it wasn’t trained on.
  
  

Put simply: if your language model is guessing, RAG helps it stop.

How Retrieval-Augmented Generation (RAG) Works

Step 1 – Retrieval: Finding the Right Information:

The system searches for relevant documents or passages using a search engine, vector database, or both. These documents are pre-processed (split into parts and embedded) so they can be quickly searched later.

Step 2 – Generation: Producing an Informed Response:

The selected content is sent to the LLM as extra context, allowing it to generate a response based on real data—not just what it “remembers.”

This process makes the LLM a smart assistant that responds with grounded, up-to-date, and useful information.

Types of Retrieval-Augmented Generation (RAG): Overview of Key Variants

There’s more than one way to implement RAG. Here are five common approaches—each one tailored to different needs and technical trade-offs:

1. Naive RAG

How it works:
Documents are split into small pieces (chunks) and turned into vector embeddings. The system finds the most relevant chunks using similarity search and feeds them to the language model.

Great for:
Simple tasks like FAQ bots, static documentation search, or answering isolated questions.

Limitations:

• Doesn’t understand relationships between sections (e.g., references to previous parts of a document).
  
  
• Can struggle with industry-specific language.
  
  
• No support for connecting related documents (e.g., PO → invoice → payment record).

2. GraphRAG

How it works:
GraphRAG builds a knowledge graph from your data : connecting related concepts and terms (e.g., “Cow” eat “plants”). This graph adds structure and meaning that traditional RAG doesn’t capture.

Great for:
Complex domains like supply chain, legal contracts, or healthcare records where relationships matter.

Limitations:

• Requires more tokens during generation due to graph traversal.
  
  
• Needs frequent re-indexing when new data is added.
  
  
• Graph management adds architectural complexity.

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3. Speculative RAG

How it works:
This strategy creates two answers in parallel:

• A quick first draft using lightweight context.
  
  
• A refined response that arrives moments later with deeper analysis.
  
  

The system can merge or replace the initial response for a smoother user experience.

Great for:
Real-time applications like product recommendations or live chat where speed matters.

Limitations:

• Requires parallel processing (higher compute cost).
  
  
• Needs logic to compare and merge outputs.
  
  
• Can waste tokens if not well-optimized.

4. Context RAG

How it works:
Instead of treating document chunks as isolated, Context RAG keeps surrounding context (like nearby paragraphs or section titles) during indexing. This helps preserve meaning and improve retrieval accuracy.

Great for:
Legal, academic, or technical documents where understanding a section requires knowing what came before or after.

Limitations:

• Larger embeddings = more storage and processing cost.
  
  
• Too much context can confuse retrieval if not tuned well.
  
  
• Updating one section may require re-embedding the rest.

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5. Hybrid RAG

How it works:
Combines both dense (vector) and sparse (keyword-based) retrieval methods. This gives the best of both worlds: semantic understanding and exact term matching.

Great for:
Enterprise use cases with mixed content types—structured data, free-form text, or when user queries don’t match document wording.

Limitations:

• More complex to build and maintain.
  
  
• Requires tuning to balance both search methods.
  
  
• Can be slower due to added re-ranking steps.

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Ready to bring AI into your business workflows?

Whether you're starting a new project or looking to optimize an existing solution, we support you from strategy to full implementation.

Let’s explore how techniques like RAG can unlock real value from your data.

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Sources

Google research on speculative RAG

Wikipedia - Retrieval augmented generation

Anthropic - Contextual retrieval

Hybrid Rag research

Microsoft - Graph RAG

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