kbdiaz
do shallow prompt injection tricks like this even work anymore on the latest models?
At my company, we use a mix of offline and online evals. I’m primarily interested in search agents, so I’m fortunate that information retrieval is a well-developed research field with clear metrics, methodology, and benchmarks. For most teams, I recommend shipping early/dogfooding internally, collecting real traces, and then hand-curating a golden dataset from those traces.
Many people run simple ablation experiments where they swap out the model and see which one performs best. That approach is reasonable, but I prefer a more rigorous setup.
If you only swap the model, some models may appear to perform better simply because they happen to work well with your prompt or harness. To avoid that bias, I use GEPA to optimize the prompt for each model/tool/harness combination I’m evaluating.
I've been building SQL agents recently, and nothing is better than just giving it access to Trino.
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- Upgrade our NodeJS version because it just got deprecated
- Upgrade our linter to the newest version, add a new rule, and fix all instances of that rule in our code
- Make minor changes to our UI
- Fix small bugs that I know how to fix, and can tell it exactly what to do
The main pain point they're solving for me is that I have many small tasks I need to do. Coding them isn't the main bottleneck, but creating a new branch and then creating a new PR is the main bottleneck for me. With cursor specifically, I don't even have to check out the branch locally to verify the code.
For any significant work, I'd rather manually do it in editor.