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    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.

    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.

    • Not a code generator for vector DB drivers.
    • Requires user-provided logs for debugging.
    • Strategy-heavy, not a one-click deployment tool.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Listed4 months ago
    Updated4 months ago

    What's inside

    Frequently Asked Questions

    This skill analyzes your data ingestion, vector database configuration, and retrieval logic to pinpoint exactly where performance is degrading, providing specific technical fixes for chunking, embedding, and reranking layers.

    It is compatible with all major vector stores including Pinecone, Weaviate, Milvus, and pgvector, and can be integrated into any developer agent or workflow that supports standard architectural modeling.

    Setup is immediate; once the skill is added to your agent, you simply provide your current tech stack and system logs to begin receiving architectural diagnoses and optimization reports.

    The skill includes a comprehensive diagnostic framework, a library of design patterns for hybrid search and query rewriting, and a structured remediation roadmap for fixing retrieval issues.

    Yes, the skill is designed to analyze live production symptoms like high latency, low relevance (recall), or hallucination rates, providing a prioritized plan for immediate remediation.

    The skill is regularly updated to include the latest RAG techniques, such as HyDE, sophisticated reranking models, and context window optimization strategies as LLM capabilities evolve.