What is RAG in AI development?

Retrieval-Augmented Generation (RAG) is a technique that provides LLMs with access to external knowledge bases, allowing them to provide accurate, grounded, and context-aware responses based on specific data.

Does it support hybrid search?

Yes, it includes patterns for combining dense retrieval (semantic similarity) with sparse retrieval (keyword matching) to achieve the most accurate search results.

Which vector databases does this skill support?

This skill provides implementation patterns for several popular vector stores including Pinecone, Weaviate, Milvus, Chroma, Qdrant, and FAISS.

How do I evaluate the quality of my RAG system?

The skill includes evaluation metrics for accuracy, retrieval quality, and groundedness to help you benchmark and improve your system's performance.

How do I reduce hallucinations in my AI app?

This skill helps reduce hallucinations by implementing grounded responses, where the LLM is constrained to answer only using the context retrieved from your trusted documents.

RAG Implementation & Optimization

Name: RAG Implementation & Optimization
Author: Berkay2002

byBerkay2002

데이터 과학 및 ML

Builds and optimizes Retrieval-Augmented Generation (RAG) systems using advanced vector search, semantic chunking, and retrieval patterns.

소개

This skill provides a comprehensive framework for implementing Retrieval-Augmented Generation (RAG) within LLM applications to ensure grounded, factual, and domain-specific responses. It covers the entire pipeline from document ingestion and semantic chunking to vector database integration and advanced retrieval patterns like hybrid search and reranking. Ideal for developers building documentation assistants, proprietary knowledge bases, or complex search systems, it offers standardized implementation patterns and best practices to minimize hallucinations and maximize retrieval precision.

주요 기능

0 GitHub stars
Support for major vector databases including Pinecone, Chroma, and Weaviate
Built-in evaluation metrics for accuracy, groundedness, and retrieval quality
Granular document chunking methods including semantic and recursive splitting
Reranking and contextual compression to improve top-k result relevance
Advanced retrieval strategies like Hybrid Search, Multi-Query, and HyDE

사용 사례

Building enterprise Q&A systems over proprietary internal documents
Implementing domain-specific semantic search for complex technical documentation
Developing research tools that provide factual answers with source citations

소개

주요 기능

0 GitHub stars
Support for major vector databases including Pinecone, Chroma, and Weaviate
Built-in evaluation metrics for accuracy, groundedness, and retrieval quality
Granular document chunking methods including semantic and recursive splitting
Reranking and contextual compression to improve top-k result relevance
Advanced retrieval strategies like Hybrid Search, Multi-Query, and HyDE

사용 사례

Building enterprise Q&A systems over proprietary internal documents
Implementing domain-specific semantic search for complex technical documentation
Developing research tools that provide factual answers with source citations