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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-small or 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,
  

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