The key is there's always going to be a huge portion of key that's in the prompt itself, but there's a lot in terms of like potentially fine tuning these models. So I would take a base model with a set of weights and then instead of like passing a lot of context in the prompt, maybe the real solution is to train further these models down the line. That library is still useful regardless of your approach in the end.
MLOps Coffee Sessions #167 with Maxime Beauchemin, Treating Prompt Engineering More Like Code.
// Abstract
Promptimize is an innovative tool designed to scientifically evaluate the effectiveness of prompts. Discover the advantages of open-sourcing the tool and its relevance, drawing parallels with test suites in software engineering. Uncover the increasing interest in this domain and the necessity for transparent interactions with language models. Delve into the world of prompt optimization, deterministic evaluation, and the unique challenges in AI prompt engineering.
// Bio
Maxime Beauchemin is the founder and CEO of Preset, a series B startup supporting and commercializing the Apache Superset project. Max was the original creator of Apache Airflow and Apache Superset when he was at Airbnb. Max has over a decade of experience in data engineering, at companies like Lyft, Airbnb, Facebook, and Ubisoft.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Max's first MLOps Podcast episode: https://go.mlops.community/KBnOgN
Test-Driven Prompt Engineering for LLMs with Promptimize blog: https://maximebeauchemin.medium.com/mastering-ai-powered-product-development-introducing-promptimize-for-test-driven-prompt-bffbbca91535https://maximebeauchemin.medium.com/mastering-ai-powered-product-development-Test-Driven Prompt Engineering for LLMs with Promptimize podcast: https://talkpython.fm/episodes/show/417/test-driven-prompt-engineering-for-llms-with-promptimizeTaming AI Product Development Through Test-driven Prompt Engineering // Maxime Beauchemin // LLMs in Production Conference lightning talk: https://home.mlops.community/home/videos/taming-ai-product-development-through-test-driven-prompt-engineering
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Timestamps:
[00:00] Max introducing the Apache Superset project at Preset
[01:04] Max's preferred coffee
[01:16] Airflow creator
[01:45] Takeaways
[03:53] Please like, share, and subscribe to our MLOps channels!
[04:31] Check Max's first MLOps Podcast episode
[05:20] Promptimize
[06:10] Interaction with API
[08:27] Deterministic evaluation of SQL queries and AI
[12:40] Figuring out the right edge cases
[14:17] Reaction with Vector Database
[15:55] Promptomize Test Suite
[18:48] Promptimize vision
[20:47] The open-source blood
[23:04] Impact of open source
[23:18] Dangers of open source
[25:25] AI-Language Models Revolution
[27:36] Test-driven design
[29:46] Prompt tracking
[33:41] Building Test Suites as Assets
[36:49] Adding new prompt cases to new capabilities
[39:32] Monitoring speed and cost
[44:07] Creating own benchmarks
[46:19] AI feature adding more value to the end users
[49:39] Perceived value of the feature
[50:53] LLMs costs
[52:15] Specialized model versus Generalized model
[56:58] Fine-tuning LLMs use cases
[1:02:30] Classic Engineer's Dilemma
[1:03:46] Build exciting tech that's available
[1:05:02] Catastrophic forgetting
[1:10:28] Promt driven development
[1:13:23] Wrap up
