AI Operations · Beyond the chatbot demo

Production AI Engineering.

Build AI features around a real workflow with explicit knowledge, tools, evaluations, human controls, observability, and a safe release path.

AccessFree · public

Structure6 modules · 4.5 hours

LevelAdvanced builder

Fieldwork6 operating artifacts

Who this is for

Product teams, technical operators, and AI-assisted builders moving from a promising prototype to a feature customers or staff can actually rely on.

Course outcome

Leave with an AI system specification, evaluation set, tool contract, risk register, launch gate, and operating scorecard.

Start from the workflow

Define the decision or task, the current human process, the failure cost, and the measurable improvement before selecting a model.

Lessons

↳Automation, assistance, recommendation, and generation patterns
↳Baseline quality, latency, cost, and business value
↳When ordinary software is the better system

Field assignment

Write an operator-intent statement and baseline the current workflow using quality, time, cost, exception rate, and consequence of failure.

Engineer context and knowledge

Control what the model can know, which source wins, how freshness is maintained, and what happens when evidence is missing.

Lessons

↳Prompt context, retrieval, structured data, and source hierarchy
↳Chunking, metadata, freshness, citations, and access control
↳Unknowns, ambiguity, sensitive knowledge, and refusal

Field assignment

Create a knowledge registry with source, authority, owner, access class, update cadence, and fallback behavior.

Constrain tools and actions

Give the system useful capabilities without giving a probabilistic component unlimited operational authority.

Lessons

↳Tool schemas, narrow permissions, validation, and deterministic guards
↳Read actions, reversible writes, approvals, and destructive boundaries
↳Retries, idempotency, audit history, and manual override

Field assignment

Specify three tool contracts with allowed inputs, validation, authorization, side effects, confirmation policy, and recovery.

Build the evaluation system

Replace demo impressions with representative cases, explicit scoring, regressions, and release thresholds.

Lessons

↳Golden sets, adversarial cases, graders, and human review
↳Accuracy, groundedness, task success, latency, cost, and escalation
↳Offline evaluation, shadow traffic, and production sampling

Field assignment

Build a 50-case evaluation set spanning routine, ambiguous, sensitive, adversarial, stale-data, integration-failure, and refusal scenarios.

Design human control

Make escalation, correction, feedback, and accountability visible parts of the product rather than emergency patches.

Lessons

↳Confidence is not certainty and scores are not explanations
↳Review queues, exception routing, approval, and appeal
↳Feedback capture, privacy, retention, and change control

Field assignment

Publish an escalation policy naming the trigger, destination, context packet, response time, decision authority, and feedback loop.

Release and operate the model layer

Ship through controlled exposure, observe real behavior, and preserve the ability to change models without rebuilding the product.

Lessons

↳Model abstraction, versioning, fallbacks, rate limits, and budgets
↳Shadow, internal, limited, and full-production release stages
↳Quality monitoring, drift, incidents, rollback, and operating review

Field assignment

Create a launch gate and 30-day scorecard with exposure stage, quality thresholds, cost guardrails, incident owners, and rollback conditions.

Next course

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Build an organic acquisition system that connects visibility to qualified calls, estimates, close rate, and booked work.

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