Resources — high-trust sources for Context Engineering

Ground every non-obvious claim in these; cite inline (<sup> → #sources). Populated before teaching. Prefer primary sources (papers, vendor engineering blogs) over secondary commentary.

Canonical / vendor engineering

• Anthropic — “Effective context engineering for AI agents” (anthropic.com/engineering). The spine of this track: context as a finite, curated resource; selection over dumping.
• Anthropic — “Building effective agents” (anthropic.com/engineering). Runtime, tool use, when to use retrieval vs not.
• Anthropic — “Introducing Contextual Retrieval” (anthropic.com/news/contextual-retrieval). Chunking + context loss; contextual embeddings + BM25 hybrid; rerank.
• Anthropic — Claude Code documentation (docs.anthropic.com). Read-on-demand context model.
• Cursor — documentation (cursor.com/docs). Codebase indexing via embeddings; retrieval.
• OpenAI — embeddings & retrieval guides (platform.openai.com/docs). Embedding-based search.

Foundational papers

• Lewis et al., 2020 — “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” (arXiv:2005.11401). The original RAG formulation.
• Karpukhin et al., 2020 — “Dense Passage Retrieval for Open-Domain QA” (arXiv:2004.04906). Dense (embedding) retrieval baseline.
• Robertson & Zaragoza, 2009 — “The Probabilistic Relevance Framework: BM25 and Beyond.” Lexical / keyword retrieval, the classic baseline.
• Liu et al., 2023 — “Lost in the Middle: How Language Models Use Long Contexts” (arXiv:2307.03172). Position bias — recall degrades for content in the middle of a long window.
• Cormack et al., 2009 — “Reciprocal Rank Fusion” (SIGIR). Combining rankings for hybrid search.

Mental-model / engineering writing

• Martin Fowler — query/CQRS and data-retrieval patterns (martinfowler.com). For the “context is the new database query” analogy and read-model thinking.
• OWASP — Top 10 for LLM Applications (owasp.org). Prompt injection via retrieved/poisoned context — relevant to the failure-modes module.

Notes on trust

• Vendor blogs describe their product’s strategy; treat product specifics (e.g. exact index type) as version-dependent and verify against current docs before teaching as fact.
• Numbers (window sizes, token counts) drift fast — teach the shape of the constraint, give concrete numbers only as illustrative “at time of writing.”