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retrieval-strategies

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Actualizado10 de mayo de 2026 a las 11:10

Route RAG retrieval quality work across hybrid search, reranking, query transformation, HyDE, Self-RAG, RAPTOR, CRAG, and Graph RAG.

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Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

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Fuente

Goodnight77

Goodnight77/rag-skills

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Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Científicos de datosOcupaciones informáticas y matemáticas·SOC 15-2051

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SKILL.md

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name	retrieval-strategies
title	Retrieval Strategies
description	Route RAG retrieval quality work across hybrid search, reranking, query transformation, HyDE, Self-RAG, RAPTOR, CRAG, and Graph RAG.
category	retrieval-strategies
tags	["retrieval","ranking","hybrid-search","rag"]
allowed-tools	["Read","Grep","Glob"]

Retrieval Strategies

Overview

Use this parent skill when the main RAG problem is search quality, ranking, recall, context selection, or evidence traceability. Route to the child skill that best matches the retrieval failure.

Problem Statement

Embedding search alone often misses exact terms, ranks weak evidence too high, or retrieves incomplete context. RAG systems need retrieval strategies that adapt to query type and evidence requirements.

Key Concepts

Hybrid search: Combine sparse and dense signals.
Reranking: Reorder broad candidate sets with stronger relevance models.
Query transformation: Rewrite, expand, or decompose user questions.
Corrective retrieval: Validate and repair weak evidence before generation.

Implementation Guide

Step 1: Diagnose the Failure

Identify whether the issue is recall, ranking, query mismatch, missing context, or poor citations.

Step 2: Select the Retrieval Pattern

Choose hybrid search for exact-term misses, reranking for noisy top-k results, query transformation for vague questions, and corrective patterns for unreliable evidence.

Step 3: Evaluate With Grounded Metrics

Measure recall at k, MRR or nDCG, citation accuracy, answer faithfulness, and latency impact.

When to Use This Skill

Improving retrieval quality in an existing RAG pipeline
Selecting a search pattern for a new RAG system
Debugging weak citations or irrelevant context

When NOT to Use This Skill

The source content has not been parsed or chunked yet
The main issue is vector database deployment
The task is pure generation without retrieval

Related Skills

Metrics & Success Criteria

Improved recall and ranking quality on evaluation queries
Better answer faithfulness and citation accuracy
Latency remains acceptable for the product workflow

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rag-skills

Goodnight77/rag-skills

Use this skill when building, debugging, or improving Retrieval-Augmented Generation systems, including chunking, vector database selection, hybrid search, reranking, multimodal RAG, code documentation RAG, retrieval latency, and production RAG architecture.

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hierarchical-chunking

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Chunk nested documents into parent-child levels so retrieval can move from broad sections to fine-grained passages.

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semantic-chunking

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Use semantic boundaries and embedding similarity to chunk text for higher-relevance retrieval.

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chunking

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Route RAG chunking decisions across semantic, hierarchical, sliding-window, contextual-header, and framework-selection strategies.

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sliding-window-chunking

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Use overlapping windows to preserve context across chunk boundaries while controlling retrieval size.

2026-05-106

optimize-retrieval-latency

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Reduce retrieval latency with caching, batching, and index-level optimization.

2026-05-106

name	retrieval-strategies
title	Retrieval Strategies
description	Route RAG retrieval quality work across hybrid search, reranking, query transformation, HyDE, Self-RAG, RAPTOR, CRAG, and Graph RAG.
category	retrieval-strategies
tags	["retrieval","ranking","hybrid-search","rag"]
allowed-tools	["Read","Grep","Glob"]

Retrieval Strategies

Overview

Use this parent skill when the main RAG problem is search quality, ranking, recall, context selection, or evidence traceability. Route to the child skill that best matches the retrieval failure.

Problem Statement

Key Concepts

Hybrid search: Combine sparse and dense signals.
Reranking: Reorder broad candidate sets with stronger relevance models.
Query transformation: Rewrite, expand, or decompose user questions.
Corrective retrieval: Validate and repair weak evidence before generation.

Implementation Guide

Step 1: Diagnose the Failure

Identify whether the issue is recall, ranking, query mismatch, missing context, or poor citations.

Step 2: Select the Retrieval Pattern

Choose hybrid search for exact-term misses, reranking for noisy top-k results, query transformation for vague questions, and corrective patterns for unreliable evidence.

Step 3: Evaluate With Grounded Metrics

Measure recall at k, MRR or nDCG, citation accuracy, answer faithfulness, and latency impact.

When to Use This Skill

Improving retrieval quality in an existing RAG pipeline
Selecting a search pattern for a new RAG system
Debugging weak citations or irrelevant context

When NOT to Use This Skill

The source content has not been parsed or chunked yet
The main issue is vector database deployment
The task is pure generation without retrieval

Related Skills

Metrics & Success Criteria

Improved recall and ranking quality on evaluation queries
Better answer faithfulness and citation accuracy
Latency remains acceptable for the product workflow