RAG & Search Engineering — Complete Reference

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

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

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.