Completed
[] Specialized model versus Generalized model
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Classroom Contents
Treating Prompt Engineering More Like Code - Promptimize and Test-Driven Development for AI
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- 1 [] Max introducing the Apache Superset project at Preset
- 2 [] Max's preferred coffee
- 3 [] Airflow creator
- 4 [] Takeaways
- 5 [] Please like, share, and subscribe to our MLOps channels!
- 6 [] Check Max's first MLOps Podcast episode
- 7 [] Promptimize
- 8 [] Interaction with API
- 9 [] Deterministic evaluation of SQL queries and AI
- 10 [] Figuring out the right edge cases
- 11 [] Reaction with Vector Database
- 12 [] Promptomize Test Suite
- 13 [] Promptimize vision
- 14 [] The open-source blood
- 15 [] Impact of open source
- 16 [] Dangers of open source
- 17 [] AI-Language Models Revolution
- 18 [] Test-driven design
- 19 [] Prompt tracking
- 20 [] Building Test Suites as Assets
- 21 [] Adding new prompt cases to new capabilities
- 22 [] Monitoring speed and cost
- 23 [] Creating own benchmarks
- 24 [] AI feature adding more value to the end users
- 25 [] Perceived value of the feature
- 26 [] LLMs costs
- 27 [] Specialized model versus Generalized model
- 28 [] Fine-tuning LLMs use cases
- 29 [] Classic Engineer's Dilemma
- 30 [] Build exciting tech that's available
- 31 [] Catastrophic forgetting
- 32 [] Promt driven development
- 33 [] Wrap up
