RAG systems resources

Knowledge

• Paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020) The original RAG paper: parametric memory (the model) plus non-parametric memory (a retrieved index). Use for: the canonical architecture and vocabulary.
• Engineering post: Contextual Retrieval (Anthropic, 2024) Prepending chunk-specific context before embedding cuts failed retrievals by 49%, 67% with reranking. Use for: chunk augmentation, hybrid embeddings + BM25 numbers.
• Docs: Embeddings (Anthropic) Embedding model selection guidance for production RAG. Use for: choosing and swapping embedding models.
• Engineering post: Building Effective AI Agents (Anthropic) Workflow-versus-agent framing. Use for: deciding when plain retrieve-then-generate is enough and when an agentic loop earns its cost.
• Paper: The Probabilistic Relevance Framework: BM25 and Beyond (Robertson & Zaragoza, 2009) Authoritative BM25 treatment; official page is paywalled, free full text via Semantic Scholar. Use for: what sparse retrieval actually scores.
• Paper: Reciprocal Rank Fusion (Cormack, Clarke & Buettcher, SIGIR 2009) The two-line fusion formula behind most hybrid search. Use for: merging dense and sparse result lists.
• Research: Evaluating Chunking Strategies for Retrieval (Chroma, 2024) Empirical comparison of fixed, recursive, semantic and cluster chunking with a token-level eval. Use for: defending a chunking choice with data.
• Paper: ColBERT (Khattab & Zaharia, SIGIR 2020) Late-interaction reranking: near cross-encoder quality at lower cost. Use for: how second-stage rankers work.
• Docs: Rerank overview (Cohere) Production cross-encoder reranking as an API. Use for: the two-stage retrieve-then-rerank pattern.
• Paper: Lost in the Middle (Liu et al., 2023) Models use context best at the start and end, worst in the middle. Use for: ordering retrieved chunks in the prompt.
• Paper: Precise Zero-Shot Dense Retrieval without Relevance Labels, HyDE (Gao et al., 2022) Embed a hypothetical answer instead of the raw query. Use for: query rewriting when queries and documents speak different languages.
• Paper: Ragas: Automated Evaluation of RAG (Es et al., 2023) and Ragas metrics docs Faithfulness, context precision, context recall, response relevancy. Use for: answer-side evaluation metrics.
• Paper: Judging LLM-as-a-Judge with MT-Bench (Zheng et al., 2023) Documented judge biases: position, verbosity, self-enhancement. Use for: trusting LLM-graded metrics the right amount.
• Textbook: Introduction to Information Retrieval, chapter 8 (Manning, Raghavan & Schütze, 2008) Free online. Use for: canonical definitions of precision, recall@k, MRR, nDCG.
• Docs: pgvector README Exact versus approximate indexes, filtering, iterative scans. Use for: combining SQL filters with vector search.
• Docs: Pinecone metadata filtering and Weaviate filters Filter syntax and semantics in dedicated vector stores. Use for: metadata filter design patterns.
• Paper: Seven Failure Points When Engineering a RAG System (Barnett et al., 2024) Failure taxonomy from real case studies. Use for: the debugging checklist.
• Guide: OWASP Top 10 for LLM Applications 2025 Prompt injection is risk LLM01. Use for: the authoritative security taxonomy.
• Series: Prompt injection (Simon Willison) Running practitioner log since 2022, including indirect injection via retrieved documents. Use for: concrete attacks and defences.

Wisdom (Communities)

• r/LocalLLaMA High-signal, hype-skeptical; strong on retriever and embedding benchmarking. Use for: stack choices and real-world RAG numbers.
• r/MachineLearning Academic bar; good paper discussion. Use for: critiques of RAGAS, HyDE and eval methodology.
• Hugging Face forums Moderated, searchable archive. Use for: embedding and eval questions with reproducible detail.
• Anthropic Discord Smaller, higher signal for Claude-specific retrieval and tool-use questions.

Gaps

• No single canonical text-to-SQL guide comparable to the vector-store filtering docs; current best practice is scattered across vendor docs.
• Public evidence on chunking for CRM-style short structured records (versus long prose) is thin; the Chroma eval covers prose corpora.