Phase 3: Enterprise RAG Systems · 60 min · Python · Cohere Reranker · Voyage AI
Retrieval Tuning on Proprietary Documents
The system returns confident answers. Most of them are wrong.
Hiring signal: Technical deep dive interviews test retrieval tuning experience: candidates who describe the iterative tuning loop (measure MRR@k, adjust chunking/embeddings/reranking, re-measure) pass. Candidates who say 'we used OpenAI embeddings and it worked' fail. Cross-encoder reranking knowledge (retrieve 50-100, rerank, keep top 5-20) is the differentiator between FDEs who've shipped RAG in production and those who've only built demos.
What you will learn
- Diagnose the silent failure: embeddings chosen in week 1, chunking at 1000 tokens, never measured
- Implement cross-encoder reranking: retrieve 50-100 candidates, rerank, keep top 5-20 for 15-25% quality gain
- Apply query rewriting: expanding queries, adding synonyms, reformulating for better retrieval match
- Handle domain-specific jargon: glossaries, fine-tuned embeddings, custom tokenization
- Run the iterative tuning loop: measure MRR@k and Recall@k, adjust, re-measure
What You'll Learn
This lesson takes approximately 60 min. By the end, you will be able to:
- Diagnose the silent failure: embeddings chosen in week 1, chunking at 1000 tokens, never measured
- Implement cross-encoder reranking: retrieve 50-100 candidates, rerank, keep top 5-20 for 15-25% quality gain
- Apply query rewriting: expanding queries, adding synonyms, reformulating for better retrieval match
- Handle domain-specific jargon: glossaries, fine-tuned embeddings, custom tokenization
- Run the iterative tuning loop: measure MRR@k and Recall@k, adjust, re-measure
The Problem
You deploy a RAG system. The client tests it. The system returns confident answers — most are wrong. The problem isn't the LLM; it's the retrieval. The right chunks aren't being surfaced, or they're ranked below irrelevant chunks, or chunk boundaries are cutting off critical context. Retrieval tuning is the iterative process of fixing these issues.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers Diagnosing the Silent Failure, Cross-Encoder Reranking, Query Rewriting, The Iterative Tuning Loop, Practical Application, What Hiring Managers Look For, Resources, Key Takeaways, Next Steps — plus a hands-on lab, quiz, and project artifact.
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