rag-implementation

Name: rag-implementation
Rating: 5 (3 reviews)
Author: carlopezzuto

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

3stars

Updated 3 months ago

View on GitHub ↗License: MIT

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/plugin marketplace add carlopezzuto/agents

The exact command may vary by repository. Check the README on GitHub.

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#llm #ai

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RAG Implementation

Master Retrieval-Augmented Generation (RAG) to build LLM applications that provide accurate, grounded responses using external knowledge sources.

When to Use This Skill

Building Q&A systems over proprietary documents
Creating chatbots with current, factual information
Implementing semantic search with natural language queries
Reducing hallucinations with grounded responses
Enabling LLMs to access domain-specific knowledge
Building documentation assistants

[Description truncada. Veja o README completo no GitHub.]

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