| name | rag-systems |
| description | Build Retrieval-Augmented Generation systems to enhance LLMs with external knowledge. Use for question answering, document search, knowledge bases, reducing hallucinations, and grounding LLM responses in factual data. |
RAG Systems
Build Retrieval-Augmented Generation systems that enhance LLMs with external knowledge.
Overview
RAG combines information retrieval with language generation, enabling LLMs to access and incorporate external knowledge for more accurate, up-to-date responses.
Quick Reference
| Scenario | Recommended Approach | Reference File |
|---|
| Document ingestion and vectorization | Chunking, embedding, indexing | /references/data-ingestion.md |
| Finding relevant information | Retrieval strategies and ranking | /references/retrieval-methods.md |
| Combining retrieval with generation | Prompt augmentation and generation | /references/generation-techniques.md |
Core Principles
- Chunking - Split documents into semantically coherent pieces
- Embedding - Convert text to dense vector representations
- Indexing - Store vectors for efficient similarity search
- Retrieval - Find most relevant chunks for query
- Augmentation - Inject retrieved context into LLM prompt
RAG Pipeline
1. Data Ingestion
- Load documents from various sources
- Split into chunks (200-500 tokens typical)
- Generate embeddings using embedding model
- Store in vector database
2. Retrieval
- Convert user query to embedding
- Perform similarity search in vector database
- Retrieve top-k most relevant chunks
- Optional: Re-rank results for relevance
3. Generation
- Construct prompt with retrieved context
- Send to LLM for generation
- Return response with optional citations
Key Components
Embedding Models:
- OpenAI text-embedding-ada-002
- Sentence Transformers (open-source)
- Domain-specific fine-tuned models
Vector Databases:
- Pinecone: Managed, scalable
- Weaviate: Open-source, feature-rich
- Chroma: Lightweight, easy to use
- FAISS: Facebook's similarity search library
Chunking Strategies:
- Fixed-size with overlap
- Sentence-based
- Paragraph-based
- Semantic chunking
Using the Reference Files
/references/data-ingestion.md — Document loading, chunking strategies, embedding generation, vector database setup, and indexing best practices.
/references/retrieval-methods.md — Similarity search, hybrid search (dense + sparse), re-ranking, metadata filtering, and retrieval optimization.
/references/generation-techniques.md — Prompt construction, context injection, citation generation, handling long contexts, and response quality improvement.
Best Practices
- Chunk documents at semantic boundaries
- Use overlap between chunks (10-20%)
- Store metadata with chunks (source, date, etc.)
- Implement hybrid search (semantic + keyword)
- Re-rank retrieved results
- Include source citations in responses
- Monitor and update embeddings regularly
- Handle edge cases (no results, too many results)
Common Pitfalls to Avoid
- Chunks too large (lose specificity) or too small (lose context)
- Not using overlap between chunks
- Ignoring metadata for filtering
- Only using semantic search (miss exact matches)
- Not re-ranking retrieved results
- Overwhelming LLM with too much context
- Not citing sources
- Stale embeddings (not updating with new data)
