rag-chatbot

Build RAG chatbots that ground answers in data using Elasticsearch retrieval.

Updated Feb 24, 2026

Search Tags:#documentation #qa #rag #retrieval #chatbot #elasticsearch #embedding

Ready to run

npx skills add https://github.com/kevinsweet/elastic-ide-context --skill rag-chatbot

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rag-chatbot
Source: https://github.com/kevinsweet/elastic-ide-context/tree/main/.cursor/skills/recipes/rag-chatbot
Command: npx skills add https://github.com/kevinsweet/elastic-ide-context --skill rag-chatbot

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Guides developers to build retrieval-augmented generation chatbots and Q&A systems using Elasticsearch as the retrieval backend, grounding responses in their own data and reducing hallucinations.

Core Features & Use Cases

Provides a four-stage architecture (Chunk, Embed & Index, Retrieve, Generate) and practical guidance for chunking, embeddings, indexing, and querying.
Supports chatbots, Q&A interfaces, and AI assistants that cite sources and reason over documents.
Includes architectural patterns, language-agnostic examples, and best practices for production-grade RAG pipelines with Elasticsearch.

Quick Start

Describe your data and run a chatbot that uses Elasticsearch to retrieve and ground answers from your documents.

Frequently Asked Questions about rag-chatbot

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a RAG chatbot using Elasticsearch as the retrieval backend?▼

To build a RAG chatbot with Elasticsearch, you implement a four-stage architecture: chunking documents, embedding and indexing data, retrieving relevant chunks, and generating grounded responses. This pattern ensures answers cite sources accurately.

What is the best way to chunk documents for retrieval-augmented generation?▼

Effective chunking for retrieval-augmented generation involves splitting documents into manageable segments while preserving context. This Skill provides practical guidance on chunking strategies and metadata indexing to optimize Elasticsearch vector retrieval.

How do I reduce LLM hallucinations when building an internal knowledge base?▼

You reduce LLM hallucinations in an internal knowledge base by grounding responses in your own data. By retrieving relevant document chunks from an Elasticsearch vector index before generation, the LLM cites sources and avoids fabricating answers.

Can I use Elasticsearch for vector search and embedding storage in a Q&A system?▼

Yes, you can use Elasticsearch for vector search and embedding storage in a Q&A system. It serves as the retrieval backend, requiring a vector index and an embedding model to store and query document representations for generation.

Does a RAG pipeline with Elasticsearch require a specific LLM integration?▼

A RAG pipeline with Elasticsearch requires an LLM integration to generate answers from retrieved context. While the Skill provides language-agnostic examples for the retrieval backend, you must connect your preferred LLM to complete the generation stage.