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- GitHub — Software collaboration platform for code repositories, reviews, issues, and development workflows.
Specialist reading: search and AI retrieval
Use these references when choosing or diagnosing components inside a search or retrieval-augmented AI feature. They explain evaluation methods rather than rating named vendors. GitHub is the catalog’s current software-collaboration listing; the reading series is not a claim that search engines or model providers have been cataloged here.
- Hybrid search explained: lexical, vector, and rank fusion — Learn when lexical and vector search complement each other, how reciprocal rank fusion works, and which costs to measure before combining them.
- How to choose an embedding model: evaluate your own corpus — Compare embedding models on held-out local queries, relevance judgments, latency, index size, and migration cost rather than leaderboard rank.
- Do you need a reranker? Quality, latency, and cost tradeoffs — Decide whether reranking can fix your search failure, with candidate-coverage checks, batch-latency arithmetic, and a cost decision table.
- Wrong RAG answers: diagnose retrieval, context, and generation — Trace a wrong RAG answer through source coverage, retrieved candidates, final context, and generation to choose the right corrective experiment.
- 6 RAG evaluation mistakes: retrieval, answers, and refusal — Build a layered RAG scorecard for evidence recall, context quality, groundedness, correctness, unanswerable questions, and over-refusal.
- Retrieval benchmarks: what BEIR and MTEB tell you — Use retrieval benchmarks to shortlist models, then check task, judgments, domain, contamination, and transfer with a local evaluation.
What to check
Check code access, review workflow, issue tracking, and automation requirements using a sample repository.
Documented features are a starting point. Check the current plan and platform, then complete a small trial before moving live work.
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