RAG Architecture & Debugging
Design, diagnose, and optimize high-performance RAG systems with an engineering-first framework.
- 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.
$12.99
· or 65 creditsSecure checkout via Stripe
Included in download
- Diagnose why an existing RAG system is hallucinating or missing facts.
- Design a scalable multi-tenant vector database schema.
- Ready for Cursor
Sample input
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?
Sample output
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.
RAG Architecture & Debugging
Design, diagnose, and optimize high-performance RAG systems with an engineering-first framework.
$12.99
· or 65 creditsSecure checkout via Stripe
Included in download
- Diagnose why an existing RAG system is hallucinating or missing facts.
- Design a scalable multi-tenant vector database schema.
- Ready for Cursor
- Instant install
Sample input
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?
Sample output
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.
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.
Use Cases
- 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.
Known Limitations
- Not a code generator for vector DB drivers. - Requires user-provided logs for debugging. - Strategy-heavy, not a one-click deployment tool.
How to Install
mkdir -p ~/.claude/skills && curl -sL https://www.agensi.io/api/install/rag-architecture-debugging -o /tmp/rag-architecture-debugging.zip && unzip -o /tmp/rag-architecture-debugging.zip -d ~/.claude/skills && rm /tmp/rag-architecture-debugging.zipFree skills install directly. Paid skills require purchase - use the download button above after buying.
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Compatible with SKILL.md-compatible agents like Claude Code, Cursor, and Aider.