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.

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

Objective

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 The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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