Chapter 22
End-to-End Project Build Plan
Learning Objective
Learn a practical step-by-step sequence for building a GenAI project from idea to production.
What it means
A GenAI project should be built in phases. Each phase proves a different risk: business value, data quality, model quality, security, integration, scalability, and operational readiness.
Healthcare Example
Start with a low-risk clinical document summarization assistant before moving to higher-risk decision-support scenarios. Validate outputs with reviewers and use the feedback to improve prompts, retrieval, and routing.
Architecture Flow
Recommended Project Sequence
- 1Define use case, users, inputs, outputs, and success metrics.
- 2Classify risk level and compliance requirements.
- 3Create reference architecture and tool selection.
- 4Collect sample documents and build evaluation dataset.
- 5Build a small prototype with prompts or RAG.
- 6Measure accuracy, groundedness, latency, and cost.
- 7Add API layer, validation, logging, and security controls.
- 8Containerize with Docker and deploy to test environment.
- 9Add CI/CD, automated tests, and security scans.
- 10Deploy to production with monitoring, alerts, and rollback plan.
Common Mistakes
- Going to production without evaluation.
- No pilot group.
- No rollback plan.
- No data governance review.
- No training for users.
Interview Q&A
Q: How would you build a GenAI project step by step?
A: I would start with use case definition, risk classification, architecture, data preparation, prototype, evaluation, secure API, CI/CD, container deployment, monitoring, and continuous improvement.
Q: What should be proven before production?
A: Accuracy, safety, cost, latency, integration reliability, security, auditability, and user acceptance.
Architect Takeaway
A GenAI project should mature from experiment to governed product. Each phase should reduce a specific risk.