When should I use HyDE instead of standard semantic retrieval?

You should use HyDE when there is a significant vocabulary mismatch between how users ask questions and how documentation is written, or when dealing with very short, abstract queries that lack enough context for direct embedding.

Does using HyDE increase search latency?

Yes, because it requires an additional LLM call before the vector search. However, this skill manages latency by using fast 'mini' models, aggressive caching, and a timeout-based fallback to standard embedding.

Is HyDE suitable for searching code snippets?

Generally, no. HyDE is most effective for natural language questions and conceptual documentation. For code-specific searches, keyword-based or direct embedding techniques usually perform better.

What is HyDE in the context of RAG?

HyDE stands for Hypothetical Document Embeddings. It is a technique where an LLM generates a 'fake' answer to a user's query, and the system embeds that answer to search the vector database, which often results in better semantic matches than searching with the query alone.

HyDE Semantic Retrieval

Name: HyDE Semantic Retrieval
Author: yonatangross

byyonatangross

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データサイエンスとML

Improves semantic search accuracy by generating hypothetical answer documents to bridge vocabulary gaps in RAG pipelines.

HyDE (Hypothetical Document Embeddings) is an advanced retrieval technique designed to enhance Retrieval-Augmented Generation (RAG) by transforming short or abstract queries into detailed, hypothetical answer documents. This process solves the 'vocabulary mismatch' problem where user search terms differ significantly from the technical terminology found in source documentation. By embedding the AI-generated answer instead of the raw query, this skill enables Claude to perform high-precision semantic search even when user intent is conceptual or non-technical, leading to significantly better context retrieval for AI agents.

主な機能

01Optimized for high-speed, low-cost models like Claude Haiku and GPT-Mini

02Hypothetical document generation to bridge semantic vocabulary gaps

03Graceful fallback to direct embedding on generation timeouts

0469 GitHub stars

05Integrated caching mechanism to minimize latency and API consumption

06Batch processing support for multi-concept query decomposition

ユースケース

01Building robust data pipelines that require high-precision document retrieval

02Enhancing semantic search for abstract or conceptual natural language queries

03Improving RAG performance for technical documentation with specialized terminology

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yonatangross/orchestkit hyde-retrieval

For use in Claude.ai and ChatGPT

Download Skill