The Unreasonable Effectiveness of Separating the Task from the Model — Maxime Rivest & Isaac Miller
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AI Engineer
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AI Engineer
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Terms in this piece · Glossary
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
Why it matters
If you're building LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → workflows, defining tasks purely by their inputs and outputs (DSPy Signatures) lets you swap models, weights, and prompt templates without rewriting your pipeline — and this covers how DSPy 4.0 extends that to programs that write code and learn from user interactions.
Key quotes
“in programming, if we want to repeat a task often, we make it a function. We believe the same should be true for AI programs.”
Maxime Rivest
“But if you want to do like AlphaGo or AlphaZero, and you tell them you're just going to learn from example, you're going to have a long night.”
Maxime Rivest
“Shopify, 550 times cheaper. They're able to do that because they went from an expensive model to a cheap model, but they could keep the same eval's, keep iterating on their business logic inside, and try new things.”
Isaac Miller
“Intelligence is very different from being all-knowing. If you were to ask Albert Einstein to help you with your emails, he'd probably ask what's an email.”
Isaac Miller
“You should define your problem, and you should hold your prompts, models, and code accountable to the problem that you need them to solve.”
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