Which vector databases does this skill support?

It provides implementation patterns and configurations for popular options including Pinecone, Chroma, Weaviate, Milvus, Qdrant, and FAISS.

Does this include advanced retrieval techniques like Hybrid Search?

Yes, it covers hybrid search (combining keyword and semantic search), multi-query retrieval, contextual compression, and parent-document retrieval patterns.

What is RAG and why should I use it?

RAG (Retrieval-Augmented Generation) allows an LLM to access external data at runtime, providing more accurate, current, and grounded answers compared to using the model's training data alone.

How does this skill help reduce AI hallucinations?

By providing specific prompt engineering templates and retrieval verification steps, it ensures the model prioritizes the provided context over its internal weights.

Can I use this for production-grade applications?

Absolutely. The skill includes best practices for metadata filtering, evaluation metrics, and monitoring essential for production-grade RAG systems.

RAG Implementation Expert

Name: RAG Implementation Expert
Author: HermeticOrmus

byHermeticOrmus

数据科学与机器学习

Builds robust Retrieval-Augmented Generation (RAG) systems to ground LLM applications in external knowledge and proprietary data.

关于

This skill provides comprehensive guidance for architecting and deploying RAG pipelines, enabling LLMs to access domain-specific information without retraining. It covers the entire lifecycle of a RAG application, from document chunking and embedding selection to advanced retrieval strategies like hybrid search and reranking. By integrating vector databases like Chroma or Pinecone, users can reduce hallucinations and build highly accurate Q&A systems, documentation assistants, and research tools grounded in verifiable source material.

主要功能

Multi-strategy retrieval including hybrid search and multi-query variations
Advanced document chunking and splitting strategies (Recursive, Semantic, Markdown)
Prompt engineering templates for contextual groundedness and source citations
Vector database integration and configuration for Chroma, Pinecone, and Weaviate
0 GitHub stars
Result reranking techniques using Cross-Encoders and MMR for improved accuracy

使用场景

Reducing LLM hallucinations in production apps through factual grounding
Developing proprietary document Q&A systems for internal company knowledge
Building AI documentation assistants that provide verifiable source citations

关于

主要功能

Multi-strategy retrieval including hybrid search and multi-query variations
Advanced document chunking and splitting strategies (Recursive, Semantic, Markdown)
Prompt engineering templates for contextual groundedness and source citations
Vector database integration and configuration for Chroma, Pinecone, and Weaviate
0 GitHub stars
Result reranking techniques using Cross-Encoders and MMR for improved accuracy

使用场景

Reducing LLM hallucinations in production apps through factual grounding
Developing proprietary document Q&A systems for internal company knowledge
Building AI documentation assistants that provide verifiable source citations