ragBuild, optimize, and debug RAG pipelines with chunking strategies, retrieval tuning, evaluation metrics, and production monitoring.
Install via ClawdBot CLI:
clawdbot install ivangdavila/ragUser wants to implement, improve, or troubleshoot Retrieval-Augmented Generation systems.
| Topic | File |
|-------|------|
| Pipeline components & architecture | architecture.md |
| Implementation patterns & code | implementation.md |
| Evaluation metrics & debugging | evaluation.md |
| Security & compliance | security.md |
Before recommending architecture, ask:
| Symptom | Likely Cause | Fix |
|---------|--------------|-----|
| Wrong docs retrieved | Query too vague, poor chunks | Query expansion, smaller chunks |
| Relevant doc missed | Not indexed, low similarity | Check ingestion, hybrid search |
| Hallucinated answers | Context too short | Increase top-k, better reranking |
| Slow responses | Large chunks, no caching | Optimize chunk size, cache embeddings |
| Inconsistent results | Non-deterministic reranking | Set seeds, use stable sorting |
Generated Feb 26, 2026
A company wants to improve its customer support chatbot by integrating internal knowledge bases and product manuals to provide accurate, context-aware answers. The RAG skill helps design a pipeline that retrieves relevant documentation chunks in real-time, ensuring responses are grounded in up-to-date information while handling varied query phrasings.
A law firm needs to quickly search through vast collections of case files, statutes, and contracts to find precedents or specific clauses. Using the RAG skill, they can implement a retrieval system with optimized chunking and hybrid search, enabling efficient document retrieval while maintaining security and compliance with legal standards.
A healthcare provider aims to build an AI assistant that retrieves medical guidelines and research papers to support diagnostic decisions. The RAG skill assists in creating a secure pipeline with PII detection and access controls, ensuring accurate retrieval of relevant medical information while adhering to HIPAA regulations.
An e-commerce platform seeks to enhance product recommendations by retrieving detailed product descriptions and customer reviews based on user queries. The RAG skill enables the design of a retrieval pipeline with tuning for top-k and reranking, improving relevance and reducing hallucinated suggestions in dynamic shopping environments.
Offer specialized consulting to help businesses design and deploy custom RAG pipelines, including architecture selection and optimization. Revenue is generated through project-based fees and ongoing support contracts, targeting industries with complex document retrieval needs like legal or healthcare.
Develop a cloud-based platform that provides tools for building, evaluating, and monitoring RAG systems, with features like automated chunking and performance dashboards. Revenue comes from subscription tiers based on usage volume and advanced features, appealing to tech startups and enterprises.
Create training courses and certifications on RAG implementation, covering topics from basics to advanced tuning for data scientists and engineers. Revenue is generated through course fees, certification exams, and corporate training packages, leveraging the growing demand for AI skills.
💬 Integration Tip
Start by building a small evaluation dataset to measure baseline performance before scaling the pipeline, and ensure consistent embedding models between queries and documents to avoid retrieval errors.
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Gemini CLI for one-shot Q&A, summaries, and generation.
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