RAG Pipeline Builder
$2.99OfficialBuild retrieval-augmented generation pipelines: chunking strategies, embedding models, vector stores, and reranking for accurate grounding.
What you get
- ✓9-step procedure
- ✓6 pitfalls to avoid
- ✓Installs into 6 tools
- Version
- v1 →
- Last updated
- today
- Length
- 5 min read
- Requires
- Best with a strong model (Claude Sonnet 4)
Works in: Claude Code, Codex, Cline, opencode, OpenClaw, Hermes · Handles multi-file projects
Preview
When to use
Use this skill when the user wants an agent or LLM to answer questions grounded in a specific corpus: internal docs, a codebase, product manuals, legal text, support tickets. Trigger phrases: RAG, retrieval-augmented, knowledge base, vector search, semantic search, grounding, "chat with my docs," document Q&A.
Do NOT use it for memory of past interactions (that's agent memory), for live data the agent fetches via tools (that's tool use), or for fine-tuning (different technique). RAG is specifically: pre-index a static/semi-static corpus, retrieve relevant chunks at query time, and condition generation on them.
Inputs to gather
- The corpus: what documents, in what
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