𝗛𝘆𝗯𝗿𝗶𝗱 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗮𝗻𝗱 𝗔𝗴𝗲𝗻𝘁 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆
Most RAG systems fail in production. They do not fail because of the language model. They fail at retrieval.
The system fails to fetch the right data chunk. Or it fetches the data but buries it at rank 40. The generator never sees the information. Your team has no way to see what went wrong.
This architecture fixes both problems.
Follow these three steps for better results:
Use Hybrid Retrieval Run lexical BM25 and dense semantic search at the same time. Use reciprocal rank fusion to merge the lists. Benchmarks show this adds 8 percentage points to Recall@5 on text and table data compared to BM25 alone.
Add a Reranker A reranker is your best way to increase precision. Use a cross-encoder on the top 50 to 100 candidates. This step improves your results significantly.
Focus on Observability You need traces to find errors in your retrieval pipeline. Without traces, you cannot fix the system.
Build your RAG system with these production standards.
Source: https://dev.to/rishi_kora/hybrid-retrieval-and-agent-observability-a-production-rag-build-2h6p
Optional learning community: https://t.me/GyaanSetuAi