Build a complete five-stage RAG pipeline (encode → chunk → index → fuse → retrieve) in pure Rust with aprender-rag. You'll wire RecursiveChunker(512, 50) for 50-character overlap that repairs query seams, MockEmbedder(384) for deterministic teaching-grade embeddings (no GPU, no model download, no network), and FusionStrategy::Rrf { k: 60 } for reciprocal rank fusion that lifts long-tail recall without learned weights. The closing demo runs aprender_film_search against a 50-row Sakila film fixture and emits top-5 JSON with four runtime assert! contracts that fire if anything drifts.

Apply the same five-stage RAG pipeline to source code instead of text. The pmat query tool indexes a workspace where chunks are functions, then layers production-grade enrichment on top: search modes (--literal for exact ripgrep-style match, --regex for pattern, semantic by default), enrichment flags (--churn for 90-day Git volatility, --duplicates for MinHash+LSH clone detection, --entropy for diversity, --faults for Batuta unwrap/panic/unsafe annotations, -G for git-history RRF fusion), and the --coverage-gaps mode that ranks every function by uncovered line count so you write tests for the highest-leverage gaps first.

Build a Final Capstone Project on RAG