Tutorials Prompt Engineering Tutorial
AI Retrieval Optimization — Complete Guide
AI Retrieval Optimization — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of Prompt Engineering Tutorial on Toolliyo Academy.
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Prompt Engineering Tutorial · Lesson 49 of 100
AI Retrieval Optimization
Prompts → Apps
Prompts · 1 — Basics · ~6 min · Module 5: RAG Systems
What is this?
Retrieval optimization tunes chunk size, topK, rerankers, and query rewriting so the right context reaches the prompt.
Why should you care?
PromptVerse Retrieval Lab A/B tests HyDE query expansion vs raw user query on golden Q&A sets.
See it live — copy this example
Copy the prompt into ChatGPT, Claude, or your LLM API playground and compare outputs.
raw = userQuery
hyde = llm("Write a hypothetical answer paragraph for: " + raw)
vec = embed(hyde)
hits = index.query(vec, k=10)
final = rerank(raw, hits).slice(0, 4)What happened?
- HyDE embeds a fake answer-shaped text — often closer to doc wording.
- Reranker uses raw query for precision on top 4.
Practice next
- Build 20 question–gold-doc pairs.
- Measure recall@4 raw vs HyDE.
- Try chunk 256 vs 512.
- Add multi-query fusion (3 paraphrases).
- Strip boilerplate before embed.
Remember
Measure recall on golden set. Tune chunk, topK, rerank. Query rewrite is fair game.
Retrieval lab win
Recall@4 stuck at 62%.
Outcome: HyDE + rerank lifts to 81% on eval set.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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