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| Issues | 35 | Founded | 2 years ago | Last Issue | 11 days ago |
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A RAG answer can fail in two ways.
The right context never reaches the model.
Or it does—and the model still gets it wrong.
If you only score the final answer, you cannot tell which one happened.
Most teams treat an LLM upgrade like this:
model = "new-model"
Change the config. Run a few tests. Ship it.
Can you switch models without breaking production?
An LLM API makes model changes look like a one-line configuratio...
Recap: part 1 was validation basics, part 2 was fields and constraints, part 3 was nested models, part 4 was config. In production, you don’t call .model_validate() by hand — a framework does it on every request. Usually, that’s FastAPI....
AI agents change the security problem.
A chatbot can give you a bad answer.
An agent can send the email, access the account, visit the website, download the file, or spend the money.
That makes a simple question much harder:
What happ...
A missing environment variable took down a production deploy at 3am. Not a bad request. Not bad user input. The app’s own configuration was wrong, and nothing caught it until the app was already live. Here’s how Pydantic stops that.
Quick...
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The writers behind this newsletter.
Senior Software Engineer & AI Architect | Deep Dives into RAG & Agentic Workflows
I've built products for over a billion users at YouTube and Google Search. Now, I deconstruct complex systems and production AI for 80,000+ engineers each week. Subscribe to learn the principles behind building at hyperscale.
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