ai-augmentation

name	ai-augmentation
description	Use to add AI/LLM features to a modernized web system — define concrete use cases, pilot them, and add evals. For this domain: message-draft generation, send anomaly detection, and natural-language operations-status summarization. Feasibility and cost of in-house LLMs are first-class concerns.

AI Augmentation

Attach AI where it removes repetitive manual work — not for novelty. Start from concrete use cases with acceptance criteria, pilot small, and guard outputs.

When to use

• After core UX/backend modernization is underway (needs stable events/APIs).
• Prefer invoking through renovate-orchestrator. If invoked directly, run the state check below first.

First action: verify renovation state

Read plan.md and confirm gates.grilling: passed. Design mode (LLM-envelope check, use-case specs, prompt design) may proceed after grilling. Code mode (shipping an AI feature) requires the harness green for every seam the feature touches, plus the test and review gates.

Method

1. Confirm the LLM envelope first. Check which LLMs are usable in the in-house/enterprise environment and their cost/latency limits. This constrains every use case — do it before designing prompts.
2. Define use cases with acceptance criteria (from requirements-grilling). Domain candidates:
   • Message draft generation — auto-draft campaign messages for author→approve flow.
   • Send anomaly detection — flag failure/delay spikes from the realtime status stream and surface likely cause.
   • NL operations summary — natural-language summary of system/send status for the ops dashboard, replacing manual VM checks and operator inquiries.
3. Pilot thin, behind flags. Ship one use case end-to-end before expanding.
4. Guard the plumbing. Validate/scheme-check outputs, add fallbacks for LLM failure/timeout, keep a human-in-the-loop for approval-affecting actions. Never let an AI action bypass the existing review/approve gate.
5. Evaluate quality. Add an eval set (golden inputs + rubric) for each use case; track accuracy/usefulness before scale-out. (Plumbing tests live in test-authoring.)

Artifacts

• docs/renovation/07-ai/ — LLM-envelope findings (models, cost, limits), use-case specs, prompt designs, guardrails, eval results.

Staged goals (30 / 60 / 100)

• 30: 2-3 AI use cases defined + technical feasibility confirmed.
• 60: 2 pilots implemented (message draft generation, NL ops summary).
• 100: expanded set (draft generation, anomaly detection, ops summary) in production.

Gates

• Requires: harness green; consumes stable events/APIs from strangler-backend.
• Produces code → must pass test-authoring and strict-code-review.

Transition

Feed measured AI impact into impact-measurement.

Output template and helper script

• Korean template: OUTPUT_TEMPLATE.md
• Scaffold script: scripts/create-ai-doc.sh <repo-root> <target-name>