I built a tool so a human could approve what an LLM does to a production database, based on measured before/after values rather than the model's summary. Then I found it was showing them the wrong set of columns.
I have been building a thing that lets a language model propose an UPDATE, then
executes it for real inside a transaction, measures the actual before and after
values, and always rolls back. A human reads the measurement and decides. Only
then does anything commit.
The pitch is one sentence: what you approve is not the model's description of
**TL;DR **LLMs are optimized for your approval, not accuracy. Changing how I frame questions — from...
Before writing any LLM logic, define your evaluation step. Here's how evals catch bad outputs early on production systems.
Large language models are surprisingly optimistic reviewers. Ask an LLM to review an implementation...
Most "AI agent" tutorials wire the model straight to execution: user asks → model decides → system...
Most of the "AI for observability" work I see right now hands the language model the judgment. I...
If you're building anything with an LLM judge in the loop, this is the failure mode that will get...
