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Last updated: August 21, 2026
Build Your First RAG Pipeline: A Step-by-Step Tutorial for AI Developers
1. Understanding Retrieval-Augmented Generation (RAG)
- What RAG is and why it overcomes LLM limitations (e.g., hallucination, stale knowledge)
- Core components: vector database, embedding model, LLM, and retrieval logic
- Real‑world use cases: customer support bots, internal knowledge bases, code assistants
2. Setting Up Your Environment & Tools
- Installing Python packages:
chromadb,langchain,openai,pypdf - Obtaining API keys for OpenAI (or a local LLM) and configuring environment variables
- Choosing a vector database: ChromaDB vs. Pinecone vs. FAISS – when to use each
3. Preparing Your Data & Generating Embeddings
- Ingesting documents: splitting text into chunks of 500–1000 tokens with overlap
- Creating embeddings using OpenAI’s
text-embedding-3-smallor a local model - Storing embeddings in ChromaDB with metadata (source, page number, date)
4. Implementing the Retrieval Engine
- Writing a function to query the vector DB and return top‑k relevant chunks
- Tuning the retrieval: similarity score threshold, chunk size, and `k` value
- Adding hybrid search (keyword + semantic) for better accuracy on specific terms
5. Connecting Retrieval with an LLM
- Building a prompt template that injects retrieved context + user query
- Calling GPT‑4o (or another LLM) via LangChain’s `ChatOpenAI` and `RetrievalQA` chain
- Handling edge cases: no relevant context, empty retrieval, fallback responses
6. Testing & Optimising Your RAG Pipeline
- Evaluating answer quality with a test set of 10–20 domain‑specific questions
- Iterating on chunk overlap,
🤖 Editor’s Pick
Editor’s Pick: AI notebook for debugging pipelines. Best for beginners learning retrieval-augmented generation workflows.
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