Pipeline design
Production retrieval is a pipeline: ingestion, chunking, metadata, embeddings, lexical search, vector search, reranking, and answer evaluation.
Retrieval Infrastructure
Hybrid search, embedding pipelines, metadata strategy, reranking, and evals for RAG systems in production.
retrieval architecture diagram. Nodes: docs, chunks, vector, lexical, rerank, eval. Flow: docs to chunks (primary); chunks to vector (primary); vector to rerank (primary); chunks to lexical (supporting); lexical to rerank (supporting); rerank to eval (feedback).
Diagnostics
Before changing models, inspect misses, ambiguous chunks, stale documents, filters, and query intent.
Retrieval Infrastructure
Embedding pipelines, hybrid search, metadata filters, reranking, and stable eval sets for RAG systems.
retrieval architecture diagram. Nodes: docs, chunks, vector, lexical, rerank, eval. Flow: docs to chunks (primary); chunks to vector (primary); vector to rerank (primary); chunks to lexical (supporting); lexical to rerank (supporting); rerank to eval (feedback).
Evaluation Systems
Regression suites, adversarial cases, trace review, cost tracking, and evidence that a system is ready to ship.
evals architecture diagram. Nodes: trace, cases, score, regress, report, fix. Flow: trace to cases (primary); cases to score (primary); score to report (primary); score to regress (feedback); regress to fix (supporting); fix to trace (loop, correction path).
Related projects
Projects where I apply this concept. The projects page shows the code and maturity of each one.
Have a project in mind?
Write to info@gianlucamazza.it with the operational problem and the systems involved. When a working prototype already exists, the first step is the production readiness review. That review does not include code changes.