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Last updated: September 15, 2026
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Building a RAG Pipeline from Scratch: A Step-by-Step Tutorial
1. Understanding the RAG Architecture
- Break down the core components: document ingestion, embedding generation, vector storage, and retrieval-augmented generation.
- Learn how RAG overcomes LLM limitations like hallucination and outdated knowledge by grounding responses in your own data.
- Identify the key trade-offs: chunk size vs. retrieval precision, and latency vs. response quality.
2. Preparing Your Knowledge Base
- Select and clean your source documents (PDFs, Markdown, HTML) – remove boilerplate, normalize formatting, and split into logical chunks (e.g., 512 tokens with 20% overlap).
- Use a chunking strategy that preserves context: semantic chunking with LangChain’s <code>RecursiveCharacterTextSplitter or spaCy sentence segmentation.
- Create metadata for each chunk (source, page number, heading) to enable filtered retrieval later.
3. Generating Embeddings & Storing in a Vector Database
- Choose an embedding model: sentence-transformers (
all-MiniLM-L6-v2) for local use or OpenAI’stext-embedding-3-smallfor cloud. - Set up a vector store – ChromaDB for prototyping or Pinecone/Qdrant for production – and index your chunk embeddings.
- Write a Python script to batch-embed chunks and upsert them into the vector database with associated metadata.
4. Implementing the Retrieval Logic
- Design a query transformation step: rephrase user queries to match the embedding space (e.g., HyDE – Hypothetical Document Embeddings).
- Implement hybrid search: combine dense vector similarity with keyword (BM25) retrieval using a weighted ensemble (e.g., 0.7 dense + 0.3 sparse).
- Set a relevance threshold (cosine similarity > 0.75) and limit top‑k results to 3–5 chunks to avoid context overflow.
5. Crafting the Prompt & Generating the Answer
- Structure the prompt template: system message + retrieved chunks (as context) + user question, with clear instructions to cite sources.
- Use a temperature of 0.2 for factual precision and experiment with
top_pto reduce hallucinations. - Add a fallback response: if
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