rag-systems

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

1. Chunking - Split documents into semantically coherent pieces
2. Embedding - Convert text to dense vector representations
3. Indexing - Store vectors for efficient similarity search
4. Retrieval - Find most relevant chunks for query
5. 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)