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RAG Pipeline (Detailed)

The complete end-to-end system for Retrieval-Augmented Generation, including document ingestion, chunking, embedding, indexing, retrieval, reranking, and generation.

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01 / Plain-language context

How RAG Pipeline (Detailed) works.

A production RAG pipeline has many more components than the basic "retrieve and generate" description suggests. Here is a complete pipeline:

Ingestion phase: (1) Document loading: parse PDFs, Word docs, web pages, databases. (2) Chunking: split documents into pieces. Chunk size matters: too small loses context, too large dilutes relevance. Common: 500-1000 tokens with 100-200 token overlap. (3) Embedding: convert each chunk to a vector using an embedding model. (4) Indexing: store vectors in a vector database (Pinecone, pgvector, Weaviate).

Query phase: (1) Query embedding: convert the user's question to a vector. (2) Retrieval: find the top-K most similar chunks (typically K=5-20). (3) Reranking: use a cross-encoder model to re-score retrieved chunks by relevance (dramatically improves quality). (4) Context assembly: combine the top chunks into a prompt with the original question. (5) Generation: LLM generates an answer grounded in the retrieved context. (6) Post-processing: add citations, validate claims, format output.

Common pitfalls: wrong chunk size, no reranking (top retrieval results are often not the most relevant), ignoring metadata (filter by date, source, category before vector search), and not evaluating quality systematically.

02 / Practical uses

Where it helps.

  1. 01Enterprise knowledge base Q&A
  2. 02Legal document analysis
  3. 03Technical support automation
  4. 04Academic research assistants
  5. 05Internal company search
03 / Keep exploring
RAG (Retrieval-Augmented Generation)A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.EmbeddingsNumerical vector representations of text that capture semantic meaning, enabling similarity search and clustering.Vector DatabaseA specialized database optimized for storing and searching high-dimensional vector embeddings, enabling semantic similarity search.Semantic SearchA search approach that finds results based on meaning rather than exact keyword matches, using embeddings to understand the intent behind queries.Embedding ModelA specialized AI model that converts text, images, or other data into numerical vectors (embeddings) that capture semantic meaning for search and comparison.