Treating Prompt Engineering More Like Code - Promptimize and Test-Driven Development for AI

Treating Prompt Engineering More Like Code - Promptimize and Test-Driven Development for AI

MLOps.community via YouTube Direct link

[] Specialized model versus Generalized model

27 of 33

27 of 33

[] Specialized model versus Generalized model

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Treating Prompt Engineering More Like Code - Promptimize and Test-Driven Development for AI

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

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