An Introduction to Retrieval-Augmented Generation

What is RAG?

Retrieval-Augmented Generation is an AI framework that enhances the capabilities of LLMs(large Language Models) by retrieving relevant information from an external data source and then feeding this information as a context into a language model to generate more accurate, informative, and appropriate responses.

How does RAG work?

Now you know the overview of what RAG is, let’s understand how it works:

Indexing:- Structures and transforms external data, so that the system can search through it efficiently later. It includes:
1. Data Collection: Gather all the data that is needed for your application.
2. Data Chunking: Breaking down large datasets into smaller, manageable units (chunks) for easier processing.
3. Document Embedding: Now, each chunk is converted into a vector (numeric representation) that captures the semantic meaning behind text.
4. Vector Storing: These vectors are stored in a vector database (such as Pinecone, Qdrant, or Weaviate) for efficient similarity search.
Retrieval:- Transforms the user’s query into a vector embedding and retrieves the most relevant chunks by comparing it with the stored embedding in the vector database. It includes:
1. Query Embedding: The user query is converted into a vector using the same embedding model used for document embedding, ensuring uniformity.
2. Similarity Search: The system compares the user’s query embedding with the stored document embedding to retrieve the most relevant chunks.
3. Top-k Retrieval: The most relevant chunks are identified based on similarity and retrieved from the vector database.
Generation: Once the relevant chunks are retrieved, along with the user’s initial query, they are fed into the language model (LLM) to generate a response. It includes:
1. Input to LLM: The retrieved text chunks and user query are passed to the LLM, providing context for the generation process.
2. Contextual Understanding: The LLM uses the provided information to understand the user’s intent and context.
3. Response Generation: The LLM synthesizes a response based on the context and generates a coherent, informative reply for the user.

Summary Table:-

Stage	What Happens
Indexing	Collect → Chunk → Embed → Store in vector DB
Retrieval	Embed query → Search vector DB → Return top-matching chunks
Generation	Combine query + retrieved context → Feed into LLM → Generate final response

Flow Chart for Visual Understanding-

Flowchart that how RAG works

Conclusion

Our overview of Retrieval-Augmented Generation (RAG) is now complete.
The fundamentals of indexing, retrieval, and generation were discussed.
We'll look at more complex RAG subjects in future blogs, such as step-back prompting, RRF, parallel query retrieval, CoT, and HyDE.
Stay tuned for more informative blogs in the future.

Introduction to Retrieval-Augmented Generation (RAG)

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