Building AI applications: Successes and Lessons
summary
A practical account of building a usable AI product: starts with a RAG prototype and chat UI, uses semantic search with header chunking and vector indices, and evaluates changes with GPT‑4 evaluator when experts are scarce. A pilot MVP extracts data from PDFs and logs, wires agents to Shopify/ads APIs, and shows measured improvement from about 60% to 70% accuracy on a 50‑question test. Constraints include privacy rules blocking hosted OpenAI use, expensive hardware for self‑hosting, and the need for MLOps and monitoring in production.
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chapters (14)
- 0:00Introduction
- 1:29AI projects focus on use cases
- 3:42Hosting and using AI models
- 5:10AI application architecture
- 7:16Steps to happy customers
- 9:25AI for text and data
- 10:49Minimum viable AI product
- 12:35Evaluating AI system quality
- 16:24Improving AI accuracy
- 19:38Optimizing RAG data processing
- 21:23Piloting AI solutions
- 22:48Production AI considerations
- 24:38MLOps role in AI success
- 25:40Start building AI today
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