ai-rag

name	ai-rag
description	RAG and search engineering — chunking, hybrid retrieval, reranking, and nDCG evaluation. Use when building retrieval-augmented generation pipelines.

RAG & Search Engineering — Complete Reference

Build production-grade retrieval systems with hybrid search, grounded generation, and measurable quality.

This skill covers:

• RAG: Chunking, contextual retrieval, grounding, adaptive/self-correcting systems
• Search: BM25, vector search, hybrid fusion, ranking pipelines
• Evaluation: recall@k, nDCG, MRR, groundedness metrics

Modern Best Practices (Jan 2026):

• Separate retrieval quality from answer quality; evaluate both (RAG: https://arxiv.org/abs/2005.11401).
• Default to hybrid retrieval (sparse + dense) with reranking when precision matters (DPR: https://arxiv.org/abs/2004.04906).
• Use a failure taxonomy to debug systematically (Seven Failure Points in RAG: https://arxiv.org/abs/2401.05856).
• Treat freshness/invalidation as first-class; staleness is a correctness bug, not a UX issue.
• Add grounding gates: answerability checks, citation coverage checks, and refusal-on-missing-context defaults.
• Threat-model RAG: retrieved text is untrusted input (OWASP LLM Top 10: https://owasp.org/www-project-top-10-for-large-language-model-applications/).

Default posture: deterministic pipeline, bounded context, explicit failure handling, and telemetry for every stage.

Scope note: For prompt structure and output contracts used in the generation phase, see ai-prompt-engineering.

Quick Reference

Task	Tool/Framework	Command/Pattern	When to Use
Decide RAG vs alternatives	Decision framework	RAG if: freshness + citations + corpus size; else: fine-tune/caching	Avoid unnecessary retrieval latency/complexity
Chunking & parsing	Chunker + parser	Start simple; add structure-aware chunking per doc type	Ingestion for docs, code, tables, PDFs
Retrieval	Sparse + dense (hybrid)	Fusion (e.g., RRF) + metadata filters + top-k tuning	Mixed query styles; high recall requirements
Precision boost	Reranker	Cross-encoder/LLM rerank of top-k candidates	When top-k contains near-misses/noise
Grounding	Output contract + citations	Quote/ID citations; answerability gate; refuse on missing evidence	Compliance, trust, and auditability
Evaluation	Offline + online eval	Retrieval metrics + answer metrics + regression tests	Prevent silent regressions and staleness failures

Decision Tree: RAG Architecture Selection

Building RAG system: [Architecture Path]
    ├─ Document type?
    │   ├─ Page/section-structured? → Structure-aware chunking (pages/sections + metadata)
    │   ├─ Technical docs/code? → Structure-aware + code-aware chunking (symbols, headers)
    │   └─ Simple content? → Fixed-size token chunking with overlap (baseline)
    │
    ├─ Retrieval accuracy low?
    │   ├─ Query ambiguity? → Query rewriting + multi-query expansion + filters
    │   ├─ Noisy results? → Add reranker + better metadata filters
    │   └─ Mixed queries? → Hybrid retrieval (sparse + dense) + reranking
    │
    ├─ Dataset size?
    │   ├─ <100k chunks? → Flat index (exact search)
    │   ├─ 100k-10M? → HNSW (low latency)
    │   └─ >10M? → IVF/ScaNN/DiskANN (scalable)
    │
    └─ Production quality?
        └─ Add: ACLs, freshness/invalidation, eval gates, and telemetry (end-to-end)

Core Concepts (Vendor-Agnostic)

• Pipeline stages: ingest → chunk → embed → index → retrieve → rerank → pack context → generate → verify.
• Two evaluation planes: retrieval relevance (did we fetch the right evidence?) vs generation fidelity (did we use it correctly?).
• Freshness model: staleness budget, invalidation triggers, and rebuild strategy (incremental vs full).
• Trust boundaries: retrieved content is untrusted; apply the same rigor as user input (OWASP LLM Top 10: https://owasp.org/www-project-top-10-for-large-language-model-applications/).

Implementation Practices (Tooling Examples)

• Use a retrieval API contract: query, filters, top_k, trace_id, and returned evidence IDs.
• Instrument each stage with tracing/metrics (OpenTelemetry GenAI semantic conventions: https://opentelemetry.io/docs/specs/semconv/gen-ai/).
• Add caches deliberately: embeddings cache, retrieval cache (query+filters), and response cache (with invalidation).

Do / Avoid

Do

• Do keep retrieval deterministic: fixed top_k, stable ranking, explicit filters.
• Do enforce document-level ACLs at retrieval time (not only at generation time).
• Do include citations with stable IDs and verify citation coverage in tests.

Avoid

• Avoid shipping RAG without a test set and regression gate.
• Avoid "stuff everything" context packing; it increases cost and can reduce accuracy.
• Avoid mixing corpora without metadata and tenant isolation.

When to Use This Skill

Use this skill when the user asks:

• "Help me design a RAG pipeline."
• "How should I chunk this document?"
• "Optimize retrieval for my use case."
• "My RAG system is hallucinating — fix it."
• "Choose the right vector database / index type."
• "Create a RAG evaluation framework."
• "Debug why retrieval gives irrelevant results."

Tool/Model Recommendation Protocol

When users ask for vendor/model/framework recommendations, validate claims against current primary sources.

Triggers

• "What's the best vector database for [use case]?"
• "What should I use for [chunking/embedding/reranking]?"
• "What's the latest in RAG development?"
• "Current best practices for [retrieval/grounding/evaluation]?"
• "Is [Pinecone/Qdrant/Chroma] still relevant in 2026?"
• "[Vector DB A] vs [Vector DB B]?"
• "Best embedding model for [use case]?"
• "What RAG framework should I use?"

Required Checks

1. Read data/sources.json and start from sources with "add_as_web_search": true.
2. Verify 1-2 primary docs per recommendation (release notes, benchmarks, docs).
3. If browsing isn't available, state assumptions and give a verification checklist.

What to Report

After checking, provide:

• Current landscape: What vector DBs/embeddings are popular NOW (not 6 months ago)
• Emerging trends: Techniques gaining traction (late interaction, agentic RAG, graph RAG)
• Deprecated/declining: Approaches or tools losing relevance
• Recommendation: Based on fresh data, not just static knowledge

Example Topics (verify with current sources)

• Vector databases (Pinecone, Qdrant, Weaviate, Milvus, pgvector, LanceDB)
• Embedding models (OpenAI, Cohere, Voyage AI, Jina, Sentence Transformers)
• Reranking (Cohere Rerank, Jina Reranker, FlashRank, RankGPT)
• RAG frameworks (LlamaIndex, LangChain, Haystack, txtai)
• Advanced RAG (contextual retrieval, agentic RAG, graph RAG, CRAG)
• Evaluation (RAGAS, TruLens, DeepEval, BEIR)

Related Skills

For adjacent topics, reference these skills:

• ai-llm - Prompting, fine-tuning, instruction datasets
• ai-agents - Agentic RAG workflows and tool routing
• ai-llm-inference - Serving performance, quantization, batching
• ai-mlops - Deployment, monitoring, security, privacy, and governance
• ai-prompt-engineering - Prompt patterns for RAG generation phase

Templates

System Design (Start Here)

• RAG System Design

Chunking & Ingestion

• Basic Chunking
• Code Chunking
• Long Document Chunking

Embedding & Indexing

• Index Configuration
• Metadata Schema

Retrieval & Reranking

• Retrieval Pipeline
• Hybrid Search
• Reranking
• Ranking Pipeline
• Reranker

Context Packaging & Grounding

• Context Packing
• Grounding

Evaluation

• RAG Evaluation
• RAG Test Set
• Search Evaluation
• Search Test Set

Search Configuration

• BM25 Configuration
• HNSW Configuration
• IVF Configuration
• Hybrid Configuration

Query Rewriting

• Query Rewrite

Navigation

Resources

• references/advanced-rag-patterns.md
• references/agentic-rag-patterns.md
• references/bm25-tuning.md
• references/chunking-patterns.md
• references/chunking-strategies.md
• references/rag-evaluation-guide.md
• references/rag-troubleshooting.md
• references/contextual-retrieval-guide.md
• references/distributed-search-slos.md
• references/grounding-checklists.md
• references/hybrid-fusion-patterns.md
• references/index-selection-guide.md
• references/multilingual-domain-patterns.md
• references/pipeline-architecture.md
• references/query-rewriting-patterns.md
• references/ranking-pipeline-guide.md
• references/retrieval-patterns.md
• references/search-debugging.md
• references/search-evaluation-guide.md
• references/user-feedback-learning.md
• references/vector-search-patterns.md
• references/graph-rag-patterns.md
• references/embedding-model-guide.md
• references/rag-caching-patterns.md

Templates

• assets/context/template-context-packing.md
• assets/context/template-grounding.md
• assets/design/rag-system-design.md
• assets/chunking/template-basic-chunking.md
• assets/chunking/template-code-chunking.md
• assets/chunking/template-long-doc-chunking.md
• assets/retrieval/template-retrieval-pipeline.md
• assets/retrieval/template-hybrid-search.md
• assets/retrieval/template-reranking.md
• assets/eval/template-rag-eval.md
• assets/eval/template-rag-testset.jsonl
• assets/eval/template-search-eval.md
• assets/eval/template-search-testset.jsonl
• assets/indexing/template-index-config.md
• assets/indexing/template-metadata-schema.md
• assets/query/template-query-rewrite.md
• assets/ranking/template-ranking-pipeline.md
• assets/ranking/template-reranker.md
• assets/search/template-bm25-config.md
• assets/search/template-hnsw-config.md
• assets/search/template-ivf-config.md
• assets/search/template-hybrid-config.md

Data

• data/sources.json — Curated external references

Use this skill whenever the user needs retrieval-augmented system design or debugging, not prompt work or deployment.

Fact-Checking

• Use web search/web fetch to verify current external facts, versions, pricing, deadlines, regulations, or platform behavior before final answers.
• Prefer primary sources; report source links and dates for volatile information.
• If web access is unavailable, state the limitation and mark guidance as unverified.