How I Work

A lightweight, repeatable process I use for shipping AI features that are useful, safe, and measurable.

1) Scope

Align on the real problem, constraints, and success metrics.
  • Stakeholders + user journey; painful steps and edge cases.
  • Define success metrics (latency p95, accuracy, containment, $/msg).
  • Pick a first slice (1 flow, 1 persona) and agree on guardrails.
Artifacts
One-page scope (problem, metrics, risks).Data/Docs inventory (sources, owners, access).Eval plan outline (golden set, scoring).

2) Prototype

Prove the end-to-end path with the fewest moving parts.
  • Stubbed tools, tiny corpus, smallest prompt that works.
  • Decide sync vs streaming; structure outputs for UI.
  • Collect logs early (inputs, outputs, latencies, costs).
Artifacts
Working demo (RAG/agent/voice) behind auth.Prompt & tool contract (JSON schemas).Latency/cost trace samples.

3) Evals

Lock in quality and safety before scaling.
  • Golden set: 50–200 real tasks with accepted answers.
  • Metrics: retrieval@k, correctness, hallucination rate, readability.
  • Red-team prompts + refusal/hand-off checks.
Artifacts
Eval notebook or script (repeatable).Dashboard of scores (pre/post changes).Go/No-Go checklist.

4) Ship

Make it reliable, observable, and cheap enough.
  • RBAC + audit trails; retry/backoff; idempotency on writes.
  • Budget prompts; cache embeddings; small/fast model where possible.
  • Roll out gradually; document handoff & on-call.
Artifacts
Runbook (alerts, quotas, retries).Prod checklist (env, keys, limits, PII).User guide (2–3 minute read).

5) Observe & Improve

Close the loop with data and user feedback.
  • Logs → weekly review (top failures, slow traces, costs).
  • Shadow tests on new prompts/models behind a flag.
  • Quarterly cleanup: dead prompts, unused tools, docs drift.
Artifacts
Weekly ops report (latency, errors, $).Backlog of improvements with estimates.Changelog (what shipped, when, impact).

Want details? See project pages for metrics, prompts, and ops notes.