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Overview
Know what breaks before you switch models. Compare your current model against a replacement using real agent traces, golden suites, semantic and tool-call evaluation, and a CI gate that fails the pull request on a regression.
News
What actually breaks when you switch LLMs
Changing the model behind an AI feature looks like the smallest change you will make all week. One string moves from gpt-x to gemini-y. The new model is cheaper, or faster, or ahead on the benchmark someone linked in Slack. You try a handful of prompts, t
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Prompt edits deserve the same gate as a model swap
A model swap gets a meeting. A prompt edit gets a commit. Both change what your product says to users, and only one of them is reviewed like it matters - usually because a prompt diff is one paragraph of English that reads fine and proves nothing. The gat
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How many eval cases do you need?
Everyone asks the question the same way: how many examples does a golden suite need? Fifty? Two hundred? The number that actually decides whether your run can say anything is not the size of the suite. It is n inside one comparison - and the suite is chop
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Evaluating agent tool calls: what text evals can't see
The output looked identical. That is the sentence at the center of most agent migration incidents. Both models answered "I've refunded your order, you'll see it in three to five business days" - and one of them called issue_refund before lookup_order, on
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Build a golden eval suite from production traffic
Every eval suite written from memory is a suite about the cases you already handle. You sit down with a blank file, imagine your users, and produce twenty prompts that look like the happy path - because the happy path is the only part of the system you ha
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