Enter the Brave New World of GenAI with Vector Search
summary
Overview of claims tools and proof: explains why generative AI still needs engineering work, shows GPT timelines (GPT‑3.5 Nov 2022, GPT models cutoff 2021) and cost tradeoffs between 3.5 and 4, and argues for RAG (retrieval augmented generation) to add up‑to‑date facts. Walks through embedding → vector database storage and similarity search, names Milvus PGVector Chroma and client libs (LangChain, LlamaIndex, Semantic Kernel, Java ports). Offers a short behind‑the‑scenes demo outline and slide/code links but no full production system or large benchmark numbers.
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chapters (11)
- 0:00Introduction
- 1:55GenAI overview
- 10:45AI evolution
- 17:41Players in the generative AI era
- 21:54Generative apps
- 27:16GenAI terms
- 32:54Working with large language models
- 36:46Retrieval augmented generation explained
- 41:01Vector search
- 46:58Benefits and challenges of GenAI
- 48:21Q&A
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