Course Objective & Scope
Bolting a chat sidebar onto legacy software (AI-enhanced) is fundamentally different from building an AI-native product where the data model, interface and value loop are architected around AI from the ground up. In AI-native teams, the PM acts as context curator, eval architect and system orchestrator.
This advanced programme covers product management for compound AI systems (multi-model routing, RAG, tools, memory and verifiers), Eval-Driven Development (EDD) team rituals and closed-loop data flywheels that turn everyday user interactions into compounding product quality.
Who Should Attend?
- Senior and Staff product managers, product directors and CPOs
- Venture, platform and innovation leads building AI-native products from scratch
- Product-minded AI engineering managers
- Prerequisite: solid product management experience and working knowledge of generative AI concepts
Key Takeaways
- Re-architect workflows from 'software that helps humans do data entry' to 'AI that executes the job while humans direct and verify'
- Run Eval-Driven Development (EDD) cycles where evals serve as living product specifications
- Design closed-loop data flywheels that capture implicit user edits into better context, memory and examples
- Collaborate effectively with AI engineers across compound systems (models, retrieval, tools and guardrails)
- Deliver an AI-native product architecture brief, eval suite specification and flywheel plan
Curriculum
From AI-Enhanced to the AI-Native Product Paradigm
- Moving from manual CRUD and form-filling software to intent- and outcome-driven products
- Evolving from a passive System of Record into an active System of Action
Managing Compound AI Systems
- Why product quality comes from orchestrating context, retrieval, routers, tools and verifiers
- Redefining the boundary and collaboration model between PMs, designers and AI engineers
Eval-Driven Product Development (EDD)
- Using golden datasets and grading rubrics as living product specifications
- Running weekly trace-review rituals and iterative quality sprints
Closed-Loop Data Flywheels and Compounding Context
- Turning user edits and approvals into dynamic few-shot examples, memory and fine-tuning data
- Designing products that get measurably smarter for each customer with use
Workshop: AI-Native Product Architecture and Blueprint
- Re-imagining an enterprise workflow as an AI-native product from first principles
- Delivering the compound system blueprint, eval plan and flywheel specification
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