Deployment, Monitoring, and Knowledge Transfer
The FDAE production deployment checklist, what monitoring means for LLM systems, the self-sufficiency test, and how to run a handoff that sticks six months after you leave.
The Production-Ready Bar
The gap between a prototype that demos well and a system that runs reliably in production is larger than most customers expect and smaller than most enterprise IT teams fear. The FDAE’s job is to close that gap specifically, not to either over-promise (“this is production-ready”) or over-qualify (“there are so many things to fix before we can ship”).
The production-ready bar for an FDAE engagement is not the same as the production-ready bar for a system that must handle millions of users. It is calibrated to the scale and risk of the specific customer workflow. For a system processing 50 documents per day with a human reviewer checking outputs, the bar is significantly lower than for an automated system making consequential decisions at scale.
Monitoring LLM Systems in Production
Monitoring for LLM systems is different from monitoring for traditional software. Traditional software either works or it does not. LLM systems can produce outputs that are syntactically valid but semantically wrong - correct JSON with incorrect content. Monitoring must account for this.
The three layers of monitoring for a production LLM system:
| Layer | What to monitor | Signal that something is wrong |
|---|---|---|
| Infrastructure | API latency, error rate, cost per call | Latency spikes, 5xx error rate above baseline, cost anomaly |
| Output quality | Schema validation pass rate, null output rate, user correction rate | Validation failures above 2%, rising correction frequency |
| Business outcome | Workflow completion rate, time saved, cases escalated to manual review | Manual review rate rising - model is degrading on real-world inputs |
Build the output quality layer into the system from day one, not as an afterthought. A logging wrapper that records each input, the raw model output, the parsed result, and whether validation passed takes a day to build and provides the data you need for every future debugging and improvement conversation. The OpenAI platform documentation and Anthropic API documentation both cover usage logging, rate limits, and token accounting that inform the infrastructure monitoring layer.
The Self-Sufficiency Test
The self-sufficiency test is the final gate of an FDAE engagement. It answers the question: can this team operate, troubleshoot, and extend this system without calling us? If the answer is yes, the handoff is complete. If the answer is no, the engagement is not finished.
Run the self-sufficiency test in a structured session with the customer team, three to five business days before the official end of the engagement - early enough to address gaps before you leave. The test has four components:
The Handoff Package
The handoff package is the artifact that makes the self-sufficiency test passable six months after you leave. It is not optional. Without it, the institutional knowledge built over the engagement evaporates when the team turns over or when a problem surfaces that no one remembers how you handled.
The minimum viable handoff package for an FDAE engagement:
- System overview (one page): what the system does, what it does not do, and where the edge cases are
- Architecture diagram: data flow from input to output, all integration points labeled with owner and failure mode
- Runbook: how to restart, how to deploy changes, how to roll back, who to call for each external dependency
- Prompt documentation: the current prompt(s), the reasoning behind key design decisions, what to test when modifying
- Known limitations: documented failure modes with specific examples and the mitigation or manual fallback for each
- Next-step roadmap: what would make the system better, in priority order, as inputs for the follow-on engagement
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