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RAG
RAG Pipeline
Definition
A RAG pipeline is the complete system for Retrieval-Augmented Generation, including document ingestion, chunking, embedding, indexing, retrieval, and generation components working together.
Why It Matters
Understanding the full RAG pipeline is essential for building effective knowledge-powered AI applications. Each component affects overall performance - weak chunking leads to poor retrieval, poor retrieval leads to irrelevant context, and irrelevant context leads to hallucinations or low-quality answers.
Pipeline Components
Ingestion Phase:
- Loading: Ingest documents from various sources (PDFs, web, databases)
- Chunking: Split documents into retrievable pieces
- Embedding: Convert chunks to vector representations
- Indexing: Store vectors in a vector database
Query Phase:
- Query Processing: Embed and optionally transform the user query
- Retrieval: Find relevant chunks via similarity search
- Reranking: (Optional) Reorder results for relevance
- Generation: Use retrieved context to generate the answer
Optimization Tips
Start simple and add complexity as needed. Measure each component separately. Common issues: chunk size too large/small, insufficient retrieval (k), or poor prompt design for the generation step.