Search & AI Search
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10 lessons3 levels1 capstone project
Design search experiences from lexical retrieval through semantic/vector and hybrid search; understand embeddings, ranking, relevance, indexing, latency and evaluation for AI-native applications.
BASIC
- What Is a Vector Database? Powering Semantic Search & AI Applications — IBM Technology (9 min)
Fast mental model for embeddings, similarity search, vector databases and why semantic search changes retrieval. - Search Is In Everything You Build — Beyond Coding (15 min selected)
Broadens search beyond text: filters, recommendations, context, hybrid search and product implications. - Vector Databases Explained — Data Science Dojo (30 min selected)
Connects embeddings, vector databases, search mechanics and real use cases before a hands-on Pinecone segment.
INTERMEDIATE
- What Is a Vector Database? Beyond Coding - Search Is In Everything You Build — IBM Technology
A deeper treatment of the same skill; use it to move from concept to working patterns. - Vector Databases Explained: Complete Guide — Aishwarya Srinivasan
Apply Search & AI Search through the adjacent capability: Vector Databases. - Production RAG Agent — Google Cloud
Apply Search & AI Search through the adjacent capability: Vector Databases.
ADVANCED
- Building Text Generation Applications — Microsoft Developer
Extend Search & AI Search into senior/production practice through Generative AI. - RAG From Scratch: Part 1 (Overview) — LangChain
Extend Search & AI Search into senior/production practice through Retrieval-Augmented Generation (RAG). - Learn RAG From Scratch — freeCodeCamp / LangChain
Extend Search & AI Search into senior/production practice through Retrieval-Augmented Generation (RAG).