SIMON
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[ lab://promptoptimize ]

Stop debugging prompts in production.
Compile them first.

The internal tooling I use to develop and stress-test LLM prompts before shipping them in MetricPilot — chained across 3 providers with deterministic fallback.

[ §01 // the problem ]

Production AI on data warehouses needs prompts that don't fail silently.

You ship a prompt to Cortex Analyst. It works in dev. In production, it hallucinates a table name. Customer sees fake numbers. Trust evaporates.

The fix is engineering discipline applied to prompts: versioning, diffable outputs, multi-provider validation, traceable failures.

No prompt-tuning UI ships that discipline by default. So I built one.

[ §02 // the approach ]

Compile, don't pray.

01

Decompose

Break your raw prompt into instruction layers. See every gap explicitly.

02

Chain & fallback

Run across Groq → Gemini → Cerebras. If one provider drops, the next picks up.

03

Diffable output

Same input, same output. Versioned. Audit-able. Production-ready.

[ §03 // try it ]

The lab is auth-gated.

Drop in a prompt. See the chain run. Inspect the divergences. Sign-in required (allowlist controls live access during private engagements).

Open the lab
[ §04 // why this matters for consulting ]

When I build AI workflows for clients on Snowflake Cortex, I bring this discipline. Prompts are versioned. Outputs are diffable. Failures are traceable.

MetricPilot — my revenue root cause engine — runs entirely on prompts developed and stress-tested here.

See MetricPilot — the product built with this discipline