context-forge-rag

Use when converting agentic AI/RAG architecture into GitHub issues, implementation specs, acceptance criteria, rollout notes, and checklists for simpler coding agents.

Use when translating ambiguous AI/RAG/product goals into principal-level architecture, ADRs, roadmap slices, and implementation-ready work for coding agents.

Use when designing or reviewing production RAG systems, including ingestion, chunking, embeddings, Pinecone namespaces, hybrid retrieval, reranking, context assembly, and routing.

Use when designing RAG/agent evaluation frameworks, golden datasets, retrieval metrics, generation scoring, latency benchmarks, regression gates, traces, and AI observability.

Use when turning strategy, architecture, or product goals into measurable technical roadmaps with phases, sequencing, dependencies, milestones, risks, staffing assumptions, and implementation-ready slices.