rag-skills

name	rag-skills
description	Use this skill when building, debugging, or improving Retrieval-Augmented Generation systems, including chunking, vector database selection, hybrid search, reranking, multimodal RAG, code documentation RAG, retrieval latency, and production RAG architecture.

RAG Skills

This skill routes RAG implementation work to the right guide in this repository. Use it when a user asks for help designing, implementing, improving, evaluating, or operating a Retrieval-Augmented Generation pipeline.

How to Use This Skill

1. Identify the main RAG problem: chunking, vector storage, retrieval quality, data type handling, latency, evaluation, agents, or deployment.
2. Open the most relevant guide under skills/.
3. Follow the guide's decision criteria, implementation notes, references, and success metrics.
4. Prefer lightweight examples in this repo, then use the linked external implementations for production code patterns.

Skill Routes

Chunking

• skills/chunking/semantic-chunking/SKILL.md: Chunk by semantic boundaries instead of fixed token windows.
• skills/chunking/hierarchical-chunking/SKILL.md: Preserve document hierarchy across sections, headings, and nested structures.
• skills/chunking/sliding-window-chunking/SKILL.md: Add overlap to preserve context near chunk boundaries.
• skills/chunking/contextual-chunk-headers/SKILL.md: Add inherited section context to chunks.
• skills/chunking/choosing-a-chunking-framework/SKILL.md: Select chunking libraries and frameworks.

Vector Databases

• skills/vector-databases/qdrant-setup-rag/SKILL.md: Set up Qdrant for RAG with metadata and filtering.
• skills/vector-databases/qdrant-for-production-rag/SKILL.md: Operate Qdrant in production RAG systems.
• skills/vector-databases/choosing-vector-db-by-datatype/SKILL.md: Choose a vector database for text, code, multimodal, and structured data.

Retrieval Strategies

• skills/retrieval-strategies/hybrid-search-bm25-dense/SKILL.md: Combine keyword and dense vector retrieval.
• skills/retrieval-strategies/multi-pass-retrieval-with-reranking/SKILL.md: Retrieve broadly, then rerank with a stronger model.
• skills/retrieval-strategies/query-transformation-strategies/SKILL.md: Rewrite, decompose, or expand queries before retrieval.
• skills/retrieval-strategies/hyde-hypothetical-document-embeddings/SKILL.md: Use hypothetical answer documents to improve query embeddings.
• skills/retrieval-strategies/hype-hypothetical-prompt-embeddings/SKILL.md: Index likely prompts or questions alongside source content.
• skills/retrieval-strategies/self-rag/SKILL.md: Add self-reflection and retrieval validation to generation workflows.
• skills/retrieval-strategies/raptor-hierarchical-retrieval/SKILL.md: Retrieve over hierarchical summaries and source chunks.
• skills/retrieval-strategies/context-enrichment-window/SKILL.md: Expand retrieved chunks with neighboring context.
• skills/retrieval-strategies/adaptive-retrieval/SKILL.md: Choose retrieval strategy dynamically based on query type.
• skills/retrieval-strategies/explainable-retrieval/SKILL.md: Improve traceability with source attribution and citations.
• skills/retrieval-strategies/crag-corrective-rag/SKILL.md: Correct weak retrieval with validation and fallback search.
• skills/retrieval-strategies/graph-rag/SKILL.md: Use graph structure and entity relationships for retrieval.

Data Type Handling

• skills/data-type-handling/rag-for-code-documentation/SKILL.md: Handle code, APIs, examples, and technical documentation.
• skills/data-type-handling/rag-for-multimodal-content/SKILL.md: Handle images, tables, diagrams, and mixed media.

Performance Optimization

• skills/performance-optimization/optimize-retrieval-latency/SKILL.md: Reduce retrieval latency with indexing, caching, and query optimization.

Evaluation Metrics

• skills/evaluation-metrics/retrieval-evaluation-metrics/SKILL.md: Measure retrieval with recall, precision, MRR, nDCG, and context precision and recall.
• skills/evaluation-metrics/generation-evaluation-metrics/SKILL.md: Score answers for faithfulness, answer relevancy, and correctness with LLM-as-judge.
• skills/evaluation-metrics/rag-evaluation-frameworks/SKILL.md: Choose an evaluation framework and wire it into a CI regression gate.

RAG Agents

• skills/rag-agents/agentic-rag-routing/SKILL.md: Route each query to the right retrieval strategy, source, or tool.
• skills/rag-agents/multi-agent-rag-orchestration/SKILL.md: Coordinate planner and specialist agents to decompose and synthesize complex answers.

Deployment

• skills/deployment/production-rag-deployment/SKILL.md: Serve, scale, and roll out a RAG pipeline safely.
• skills/deployment/index-versioning-and-reindexing/SKILL.md: Version the index and reindex or migrate embeddings without downtime.

Success Criteria

• The selected RAG pattern matches the user's actual bottleneck.
• Retrieval quality improves without adding unnecessary architecture.
• The implementation keeps metadata, evaluation, and production constraints in view from the start.
• External references are used for real implementation details instead of copying large code blocks into this skill.