TextFoundry

Use when the user wants the rollout and ownership plan for an ML or LLM delivery: scope, workstreams, dependency map, rollout steps, blockers, readiness checklist, and transition notes.

Use when the user wants a quality and release-readiness plan for an ML or LLM service: dimensions, thresholds, datasets, review process, regression rules, and post-release quality review.

Use when the user wants the operational document for an ML or LLM service: ownership, alerts, common failures, incident response, degradation modes, recovery checks, and escalation contacts.

Use when the user wants the integration contract for an ML or LLM service: API/tool methods, input/output schema, data contract rules, prompt hierarchy, retries, confidence routing, and fallback behavior.

Use when the user wants to record one concrete architectural decision in an ML or LLM system, including alternatives, chosen option, rationale, risks, and impacted artifacts.

Use when the task is to create, update, sequence, or review the ML/LLM delivery document set as a pipeline rather than a single document. Covers PRD, system design, ADR, service spec, evaluation plan, delivery plan, runbook, security note, and prompt template.

Use when the user wants to review completeness, gaps, or consistency across the full ML/LLM document set, especially links between PRD, system design, ADR, service spec, evaluation, runbook, security, and prompt artifacts.

Use when the user wants a service PRD or PRD-lite for an ML or LLM feature, agent, retriever, evaluation service, or internal ML capability. Focus on business problem, scope, users, KPI, and release acceptance.

Use when the user wants a production-grade prompt document for an ML or LLM service, including structured output contract, semantic rules, input variables, system prompt, and output examples.

Use when the user wants a security and compliance note for an ML or LLM service, including PII, storage and retention, access model, logging restrictions, vendor limits, and required security controls.

Use when the user wants an end-to-end architecture document for an ML or LLM service, including components, integrations, workloads, data flow, NFR, and degradation modes.