Rag Architect

    2

    Design, debug, and optimize production RAG systems with expert architecture, hybrid search, and grounding strategies.

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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    rag-architect

    Example session with this skill installed

    Our RAG system isn't finding specific technical errors like 'error 402' in the logs, even though the docs are indexed. Can you diagnose why retrieval is failing for these identifiers?

    • Read your context and instructions
    • Compiled the rag-architect

    Diagnosis: Low recall@k.
    Hypothesis: Missing BM25/keyword search for technical identifiers.
    Evidence: Search 'error 402' returns generic HTTP docs, not specific logs.
    Fix: Implement Hybrid Search with RRF + Metadata filters for log levels.
    Expected Impact: +25% precision on technical queries.

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

    About this skill

    Advanced RAG System Architecture & Debugging

    Designing a production-ready Retrieval-Augmented Generation (RAG) system requires more than just a vector database and a prompt. The RAG Architect skill provides a developer-centric framework for building, hardening, and troubleshooting complex retrieval stacks, moving beyond generic implementations to high-performance architecture.

    What it does

    This skill acts as a senior systems architect for your AI pipeline. It analyzes ingestion workflows, document parsing, chunking strategies, embedding selection, and vector store performance. Whether you are building from scratch or fixing a broken implementation, it applies a rigorous, evidence-based methodology to ensure your agent stays grounded and accurate.

    Supported Capabilities

    • Architecture Design: Decisions for hybrid search, reranking, and context packing tailored to your specific corpus (Legal, Code, Product Docs, etc.).
    • Truth-First Debugging: Systematic isolation of failures across the pipeline—from bad parsing to stale indexes and tenant leakage.
    • Infrastructure Selection: Unbiased tradeoff analysis for vector databases (pgvector, Qdrant, Milvus), embedding models, and rerankers.
    • Production Hardening: Implementing multi-tenant isolation, citation grounding, and incremental re-indexing strategies.
    • Evaluation Frameworks: Establishing metrics for recall@k, precision, and faithfulness to ensure changes are data-driven rather than anecdotal.

    Why use this skill?

    Standard LLM prompts often treat "bad answers" as model hallucinations. This skill identifies when the problem is actually a metadata filter mismatch, poor chunking semantics, or an inefficient reranker. It helps you reduce latency and cost by optimizing the weakest stage of your pipeline rather than over-relying on expensive long-context windows.

    • Cannot perform the actual vector DB migration or infrastructure provisioning.
    • Effectiveness is limited without access to specific log samples or retrieval metrics.
    • Does not generate frontend UI.

    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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    Listed7 months ago

    What's inside

    Frequently Asked Questions

    This skill identifies and fixes common production bottlenecks such as low retrieval precision, poor document chunking, inefficient hybrid search configurations, and hallucination issues caused by weak grounding.

    The RAG Architect is designed to work with any agentic framework that utilizes retrieval pipelines, offering architecture-agnostic advice that applies to various vector databases and embedding models.

    You receive a specialized architectural framework that includes diagnostic checklists, configuration templates for hybrid search, and a suite of evaluation strategies for your retrieval pipeline.

    No, this skill focuses on the architecture and logic of yours systems; you can apply its strategies to any vector database (like Qdrant or Milvus) and any LLM provider you prefer.

    Yes, the skill includes guidance on implementing secure multi-tenant isolation, citation-based grounding, and cost-efficient scaling strategies for production environments.

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