Customer AI Deployment

Frame the customer outcome, workflow, assumptions, and smallest useful test.

You'll build: A workflow brief with outcome, evidence, assumptions, risks, and an executable slice specification.

1. Define the Outcome Before You Choose the Output~1h
2. Discover the Workflow People Actually Perform~1h
3. Map Assumptions and Resolve the Riskiest One First~1h
4. Choose the Smallest Slice That Can Change the Decision~1h
5. Write Specifications That Preserve Judgment~1h

Build and evaluate the narrow AI system against explicit success criteria.

You'll build: A working vertical slice with an evaluation set, baseline, and success scorecard.

1. RAG (Retrieval-Augmented Generation)~2h
2. Evaluation & Testing LLM Applications~1h
3. Building a Production LLM Application~2h
4. Design Success Metrics Before the Result Exists~1h

Choose the right release stage and move the pilot through controlled exposure.

You'll build: A measured pilot with release gates, rollback conditions, and recorded results.

1. Choose Prototype, Pilot, or Production Deliberately~1h
2. Shadow Traffic, Canary Rollout, and Progressive Deployment for LLMs~1h

Show how evidence and user feedback determine the next iteration.

You'll build: A concise case study that connects user evidence, system results, tradeoffs, and the next decision.

1. Build a Feedback Ratchet with Ownership and Retirement~1h