Design a search system combining keyword and semantic search.
Hybrid Search Architect
BM25, Vector Search, RAG Systems
Best for
- ▸Designing e-commerce search that combines product name matching with semantic similarity
- ▸Building RAG systems that balance exact citation retrieval with conceptual document matching
- ▸Optimizing knowledge base search for customer support with both keyword and intent understanding
- ▸Creating code search systems that find exact function names and semantically related implementations
What you'll get
- ▸Multi-stage architecture diagram with BM25 candidate retrieval, dense vector scoring, RRF fusion weights, and cross-encoder reranking with specific latency budgets
- ▸Embedding model comparison matrix with MTEB scores, dimensionality trade-offs, and domain-specific fine-tuning recommendations
- ▸Production deployment guide with vector database selection, indexing strategies, and A/B testing framework for relevance optimization
Detailed search requirements including corpus type, query patterns, latency constraints, and relevance priorities with sample queries and expected results.
Complete hybrid search architecture with specific technology stack recommendations, fusion strategies, reranking pipelines, and implementation guidance with performance benchmarks.
What's inside
“You are a Hybrid Search Architect. You design and implement production-grade retrieval systems combining BM25, dense vector search, and multi-stage reranking pipelines for RAG and search applications. - **Configuration-first recommendations.** Every architecture recommendation includes concrete para...”
Covers
Not designed for ↓
- ×Simple keyword-only search implementations without semantic components
- ×Training embedding models from scratch or fine-tuning transformers
- ×General database query optimization unrelated to search relevance
- ×Building recommendation systems based on collaborative filtering
SupaScore
88.88▼
Evidence Policy
Standard: no explicit evidence policy.
Research Foundation: 10 sources (2 academic, 6 paper, 2 official docs)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
content refresh 2026-07: freshness review findings fixed (stale claims, invented precision, missing 2026 practice)
v6.0 wave-1 repair: re-distilled from masterfile/v2 (truncation incident 2026-06, delta-first rules)
v5.5 final distill
Pipeline v4: rebuilt with 3 helper skills
Initial release
Works well with
Need more depth?
Specialist skills that go deeper in areas this skill touches.
Common Workflows
Production RAG System Development
Design hybrid retrieval, integrate with RAG pipeline, then build evaluation framework for end-to-end relevance testing
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