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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    RAG Architecture & Debugging

    1

    Covers hybrid search design, HyDE and query rewriting, context window management, and hallucination root-cause attribution.

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    RAG Architecture & Debugging

    RAG Architecture & Debugging

    Example session with this skill installed

    My RAG system is retrieving the right documents, but the LLM still keeps hallucinating details that aren't in the text. How do I fix this?

    • Read your context and instructions
    • Compiled the rag architecture

    Your failure is at the Generation layer.

    Recommended Fixes:

    1. Tighten Prompting: Add a strict "Only answer from context" rule.
    2. Temperature: Lower to 0.0.
    3. Source Weighting: Instruct the model to cite specific chunk IDs to force grounding.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Diagnose why an existing RAG system is hallucinating or missing facts.Design a scalable multi-tenant vector database schema.Optimize chunking strategies for complex technical documentation.Implement hybrid search using BM25 and Reciprocal Rank Fusion.Setup an evaluation framework using Ragas to measure system accuracy.My RAG app returns irrelevant chunks and I don't know whyChoose a vector database for retrieval-augmented generation

    About this skill

    Your RAG pipeline is only as good as its weakest layer. This skill diagnoses exactly which layer is failing — chunking strategy, embedding mismatch, retrieval scoring, reranking, or prompt construction — and prescribes a fix with implementation steps. Covers hybrid search design, HyDE and query rewriting, context window management, and hallucination root-cause attribution. Whether you're building from scratch on Pinecone/Weaviate/pgvector or debugging a system already in production, give it your stack and symptoms and it returns a root-cause report with a prioritised remediation plan. Specify the system type (chatbot, search, knowledge base) and your stack — no vague advice, specific architectural decisions with tradeoffs explained.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
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      Download the ZIP

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      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

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    Verified clean 2 months ago

    • Passed all security checks, Safe to install

    Listed3 months ago
    Updated21 days ago

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