RAG: Building Knowledge-Enhanced AI Applications

What is RAG?

Retrieval-Augmented Generation (RAG) combines retrieval systems with generative AI to produce responses grounded in specific knowledge bases.

Why RAG?

LLMs have limitations:

Knowledge cutoff dates

Can hallucinate facts

No access to proprietary data

RAG addresses these by:

Retrieving relevant documents

Providing context to LLM

Grounding responses in facts

RAG Architecture

Components

**Document Store**: Your knowledge base

**Embedding Model**: Converts text to vectors

**Vector Database**: Stores and searches embeddings

**Retriever**: Finds relevant documents

**Generator**: LLM produces final response

Process

User query received

Query converted to embedding

Implementation Steps

1. Prepare Documents

Collect relevant content

Clean and structure

Chunk appropriately

2. Create Embeddings

Choose embedding model

Generate document embeddings

Store in vector database

3. Build Retrieval

Configure similarity search

Set retrieval parameters

Test retrieval quality

4. Integrate Generation

Prompt engineering

Context formatting

Response generation

Best Practices

Chunking Strategy

Right chunk size

Overlap for context

Maintain semantic units

Retrieval Optimization

Hybrid search

Reranking

Query expansion

Generation Quality

Clear prompts

Citation of sources

Hallucination detection

Tools and Frameworks

LangChain

LlamaIndex

Haystack

Pinecone, Weaviate, Chroma

Conclusion

RAG enables building AI applications with accurate, up-to-date, and verifiable responses.