Building RAG pipelines: retrieval augmented generation for production

Retrieval Augmented Generation combines the reasoning capability of LLMs with the factual accuracy of a retrieval system. Instead of relying solely on the model's training data, RAG retrieves relevant documents from your knowledge base and provides them as context to the LLM. This produces more accurate, up-to-date responses.

The retrieval component indexes your documents and finds the most relevant ones for each query. Use embedding models to convert text into vector representations. Store embeddings in a vector database like Pinecone, Weaviate, or pgvector. Retrieve the most similar documents using cosine similarity or other distance metrics.

The generation component takes the retrieved documents and the user's query and generates a response. The LLM uses the provided context to answer the question. If the context doesn't contain the answer, the model should say so rather than hallucinating. Configure prompts carefully to enforce this behavior.

Chunking strategy significantly impacts retrieval quality. Split documents into chunks of appropriate size too small misses context, too large includes irrelevant information. Overlap chunks to ensure no information is lost at boundaries. Experiment with different chunk sizes for your specific use case.

Evaluation is critical for RAG systems. Measure retrieval accuracy with precision and recall at different k values. Measure generation quality with human evaluation or LLM-as-judge approaches. Set up a test set of queries with ground-truth answers and track metrics over time.

Latency in RAG systems comes from two sources: retrieval and generation. Optimize retrieval with smaller indexes and approximate nearest neighbor search. Use smaller, faster models for simple queries. Cache common queries. Monitor end-to-end latency and optimize the slowest component.

Start with a simple RAG pipeline and iterate. Use a managed vector database, a good embedding model, and a capable LLM. Measure baseline performance, then improve chunking, retrieval, and prompting. Most RAG improvements come from better data preparation and retrieval, not better generation.

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Rizwan Saleem | https://rizwansaleem.co