Free Self-Assessment
Twelve yes/no checks, about two minutes. They come straight from the failures we've debugged in real production AI systems — the silent stalls, the confident-but-wrong outputs, the cost blowups nobody saw coming.
No email required. Nothing is sent anywhere — your answers are scored right here in your browser.
Does every LLM and agent call have an explicit timeout tuned to its expected latency — not just the SDK default?
When a step fails or falls back to a worse path, does your system surface it loudly instead of reporting success?
Can you trace a single request end-to-end across every model and tool call when something goes wrong?
For anything audited or repeated, do you pin temperature and settings so the same input yields the same output?
Is per-request LLM spend metered inside the client and attributable, so a cost blowup is visible before the invoice?
Do you require the model to cite its sources, and treat confident-but-uncitable answers as uncertain?
Are your critical business rules enforced in code with hard gates — not just requested in the prompt?
When you change a prompt or swap a model, do evals catch regressions before they reach production?
If you use an LLM to evaluate outputs, is there a deterministic floor and a human final call — not just model-grading-model?
If you bump a model or embedding version, do you have a backfill/migration plan so old records stay valid?
Do you avoid chaining a lossy “normalizer” model in front of another model where raw input would work better?
For high-volume work, have you moved deterministic logic off the per-request model path?