Semantic Search at Scale: What I Learned Building RAG Infrastructure at Microsoft Copilot

The article by Nrk Raju Guthikonda provides valuable insights into building robust Retrieval-Augmented Generation (RAG) systems, drawing from his experience working with large-scale search infrastructure at Microsoft. Here are the key takeaways and lessons learned:

Key Takeaways

1. Chunking Strategy:

   • Effective chunking of documents is crucial for optimizing retrieval performance.
   • Use techniques like sliding window or overlapping chunks to ensure comprehensive coverage.

2. Re-ranking:

   • Always re-rank retrieved documents using a robust model (e.g., T5).
   • The cost of re-ranking is minimal compared to the quality improvement it provides.

3. Hybrid Search:

   • Combine semantic search with keyword-based search to improve recall and precision.
   • Use techniques like Reciprocal Rank Fusion for better results.

4. Evaluation Metrics:

   • Measure retrieval metrics (Recall@K, MRR, NDCG).
   • Evaluate generation quality using metrics like faithfulness, relevance, and completeness.
   • Assess end-to-end performance with user satisfaction and task completion rate.

5. Start Local:

   • Begin by building and testing RAG systems on a single machine to

Read the full article at DEV Community

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