llmops

Prompts buried in source code mean every wording tweak is a full deploy, and nobody knows which prompt is live. Treating prompts as managed, versioned configuration is the fix. Here is the platform.

You cannot improve an LLM app you cannot see into, and LLM observability is not service monitoring. The signal is tracing multi-step chains, capturing inputs/outputs/tokens/cost, and online quality signals, not just latency and errors.

Caching is one of the biggest LLM cost levers, but 'cache the response' is naive for a non-deterministic system. The signal is distinguishing prompt (prefix) caching from semantic caching and knowing when each is safe.

LLMOps is not MLOps with bigger models. The signal is the genuinely new surfaces (prompts as deployable artifacts, eval-driven development, often no training step) and treating prompts with the same version discipline as code.

Editing a prompt is a code change with no compiler and no unit test by default, so quality regressions ship invisibly. Treating prompts and models as testable artifacts is what separates a toy from a product. Here is the harness.

Eyeballing a few outputs does not scale, and a prompt tweak that fixes one case quietly breaks ten. A real LLM eval pipeline is a versioned dataset, layered scorers, and a CI gate. Here is the architecture.