intermediateCoding & Development

Hybrid Search Implementation

Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.

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76OpxScoreProvisional
Community resultNot enough feedback0 votes
Model evidenceNo verified tests
ClaudeChatGPTGemini+5

Score breakdown

Estimated from the available content and source signals.

Provisional
Documentation82
Practical value74
Evidence63
Source trust80

Model compatibility

Inferred fit is not the same as a recorded hands-on test.

ClaudeinferredThe skill uses model-agnostic prompt or LLM terminology.
ChatGPTinferredThe skill uses model-agnostic prompt or LLM terminology.
GeminiinferredThe skill uses model-agnostic prompt or LLM terminology.
CopilotinferredThe skill uses model-agnostic prompt or LLM terminology.
LlamainferredThe skill uses model-agnostic prompt or LLM terminology.
PerplexityinferredThe skill uses model-agnostic prompt or LLM terminology.
MistralinferredThe skill uses model-agnostic prompt or LLM terminology.
GrokinferredThe skill uses model-agnostic prompt or LLM terminology.

Overview

Hybrid Search Implementation

Patterns for combining vector similarity and keyword-based search.

When to Use This Skill

  • Building RAG systems with improved recall
  • Combining semantic understanding with exact matching
  • Handling queries with specific terms (names, codes)
  • Improving search for domain-specific vocabulary
  • When pure vector search misses keyword matches

Core Concepts

1. Hybrid Search Architecture

Query → ┬─► Vector Search ──► Candidates ─┐
        │                                  │
        └─► Keyword Search ─► Candidates ─┴─► Fusion ─► Results

2. Fusion Methods

MethodDescriptionBest For
RRFReciprocal Rank FusionGeneral purpose
LinearWeighted sum of scoresTunable balance
Cross-encoderRerank with neural modelHighest quality
CascadeFilter then rerankEfficiency

Templates and detailed worked examples

Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.

Best Practices

Do's

  • Tune weights empirically - Test on your data
  • Use RRF for simplicity - Works well without tuning
  • Add reranking - Significant quality improvement
  • Log both scores - Helps with debugging
  • A/B test - Measure real user impact

Don'ts

  • Don't assume one size fits all - Different queries need different weights
  • Don't skip keyword search - Handles exact matches better
  • Don't over-fetch - Balance recall vs latency
  • Don't ignore edge cases - Empty results, single word queries

Best for

  • Building RAG systems with improved recall
  • Combining semantic understanding with exact matching
  • Handling queries with specific terms (names, codes)
  • Improving search for domain-specific vocabulary
  • When pure vector search misses keyword matches

Tips and best practices

  • Review the source instructions and adapt inputs before running the workflow.

What This Skill Can Do

AI-generated examples showing real capabilities

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