Title
One-line summary
Browse, search, and query engineering lessons harvested from multiple GitHub repositories. Features full-text search, AI-powered chat with RAG, corpus gap detection, and multi-cloud deployment.
One-line summary
When local services are already running, skip mocks and test the real pipeline end-to-end
Abstract base classes with minimal interfaces let the same RAG pipeline run on four different cloud providers without conditional logic in business code.
Systematic triage of code review findings produces a traceable requirements document — turning ad hoc observations into prioritized, implementable work.
A repeatable workflow — Design, PDR, Plan, Execute, Commit — with table-driven task tracking and one-commit-per-phase discipline, applied across 18 project phases.
Deferring cloud SDK imports to runtime lets the same codebase run with or without any given SDK installed, and enables testing without real dependencies.
Three cloud stacks (AWS, Azure, GCP) built in separate phases with OIDC federation, avoiding cross-cloud coupling while sharing a common authentication pattern.
Splitting documents at H2 headings with stable IDs and content hashes produces predictable, debuggable chunks that support incremental re-indexing.
Seven heuristic rules detect when a RAG corpus can't answer a query, without training data or a classifier — transparent and debuggable, with known trade-offs.
Designing a GitHub Actions workflow that harvests, validates, builds, indexes, and deploys a static site.