Lilly Tech Systems
Designing AI Search Engines
Master the architecture of modern search systems — from keyword matching to semantic retrieval to hybrid pipelines. Learn how to build search engines that understand user intent, rank results intelligently, and scale to billions of documents with sub-100ms latency.
Your Learning Path
Follow these lessons in order to design a complete AI-powered search system, or jump to any topic you need right now.
1. Search Architecture Evolution
From keyword search to semantic search to hybrid. BM25 vs vector search, core search system components, and real-world architecture examples from Google, Amazon, and Spotify.
2. Indexing Pipeline Design
Document processing, embedding generation at scale, incremental indexing, index sharding strategies, near-real-time indexing, and Elasticsearch + vector plugin setup with production code.
3. Retrieval & Ranking Pipeline
Multi-stage retrieval, BM25 + vector hybrid scoring with RRF and linear combination, cross-encoder re-ranking, and learning-to-rank with search features and code examples.
4. Query Understanding
Query classification, intent detection, query expansion, spell correction, entity recognition, synonym handling, and LLM-powered query rewriting with production implementations.
5. Search Personalization & Context
User history integration, contextual ranking, location-aware search, session-based personalization, and privacy-preserving personalization with differential privacy.
6. Scaling Search Infrastructure
Elasticsearch/OpenSearch cluster design, vector index scaling to billions, caching strategies, geo-distributed search, and latency optimization for sub-100ms at scale.
7. Best Practices & Checklist
Search quality metrics (MRR, NDCG), A/B testing search, relevance tuning process, production deployment checklist, and a comprehensive FAQ for search engineers.
What You'll Learn
By the end of this course, you will be able to:
Design Search Architectures
Architect end-to-end search systems with hybrid retrieval, multi-stage ranking, and query understanding pipelines that handle millions of queries per day.
Build Production Pipelines
Implement indexing, retrieval, ranking, and query understanding code using Python, Elasticsearch, and vector databases you can deploy at work tomorrow.
Optimize Relevance & Latency
Measure search quality with MRR and NDCG, run A/B tests on ranking changes, and optimize for sub-100ms response times at billions of documents.
Personalize & Scale
Add user-aware ranking, session context, and location signals while scaling horizontally across regions with geo-distributed search clusters.