Optimizing RAG Pipelines: A Deep Dive

Retrieval-Augmented Generation (RAG) is a powerful technique for grounding LLMs in external data. However, building a performant RAG pipeline is challenging.

Key Strategies

1. Chunking Strategies

The way you chunk your documents significantly impacts retrieval quality. Experiment with different chunk sizes and overlap to find the sweet spot for your data.

2. Hybrid Search

Combine keyword search (BM25) with semantic search (embeddings) to capture both exact matches and conceptual similarities.

3. Reranking

Use a cross-encoder model to rerank the retrieved results, ensuring the most relevant chunks are passed to the LLM.

Measuring Success

Use SupaEval to benchmark your RAG pipeline's precision and recall, and iterate based on data-driven insights.