Syllabus - The AI PM Builder Course

The role of the Product Manager is undergoing a fundamental shift. Traditional PMs defined static problems, wrote deterministic PRDs, aligned stakeholders, and handed requirements over to engineering. In the AI era, that is no longer enough.
AI changes both the products we build and the way product teams operate. PMs can now leverage advanced AI assistants and agentic engineering workflows to independently synthesize user research, generate product concepts, spin up interactive UI components, simulate edge cases, and build fully functional software prototypes.
However, moving a feature from a flashy demo to a stable production system introduces critical structural challenges. Modern AI applications are moving away from single model calls and shifting toward autonomous, multi-step agentic workflows. Because these systems are inherently probabilistic and possess a high degree of agency, they are exceptionally difficult to test, expensive to scale, and heavily exposed to novel risks like runaway token loops, data exposure, and alignment failures.
To ship successfully, product teams cannot rely on traditional software QA. They must master a new operational stack: tracking trajectory success metrics, engineering golden datasets for LLM-as-a-judge evaluations, hardcoding agent circuit breakers, and breaking down complex token COGS into precise input, output, and retrieval buckets.
This program is built specifically for experienced product leaders who want to transition into AI PM Builders—professionals capable of mastering the toolsets, financial models, and risk frameworks required to steer autonomous, agentic systems safely from concept to production.

How do we turn an AI opportunity into a clear product concept, testable PRD, and prototype plan?
Core Hands-on Competencies

• AI Capability Scoping: Selecting optimal product surfaces using Opportunity Solution Trees and Jobs-to-be-Done frameworks, scoping features as assistants, co-pilots, autonomous agents, or discrete background automations.
• AI-Native PRD Engineering: Structuring system requirements designed to be parsed equally by human engineers and AI coding agents. This includes defining explicit model behavior requirements, input/output data expectations, comprehensive edge-case boundaries, privacy constraints, and evaluation expectations.

Additional topics that will be shortly reviewed:

• Design decisions for AI user experiences, including evaluating when to automate processes silently vs. when to require user confirmation, displaying model confidence metrics, exposing system reasoning parameters, and implementing native user recovery controls (undo, edit, retry, and escalation paths).

Hands-on Workshop: Participants use Claude to parse raw qualitative user feedback logs, map out a high-leverage product feature, and write a comprehensive, machine-readable AI-Native PRD optimized for code generation.
Session Deliverable: AI-Native PRD + Prototype Scope Document.

How do we inject mock intelligent behaviors, capture analytical interactions, and gracefully handle edge cases within the validation prototype?
Core Hands-on Competencies

• Mock Behavioral Injection: Writing and embedding fake backend logic, designing mock APIs, and configuring simulated real-time model behaviors or text-streaming simulations.
• Edge Case Implementation: Directing Claude Code to handle interactive failure states, programmatic error messages, and system fallback routines directly inside the prototype application.
• AI Code Refactoring & Auditing: Using Claude Code to systematically inspect, debug, and optimize generated files; mapping out product analytics logs to capture user behavioral events; compiling a clean developer handoff package.

Additional topics that will be shortly reviewed:

• Using real user validation data from prototype interaction testing to refine and adjust the primary PRD before production engineering cycles begin.

Hands-on Workshop: Participants wire up simulated backend behaviors, embed realistic model output flows, handle interface errors, and generate an automated developer-ready README file detailing system states.
Session Deliverable: Functional AI Product Prototype (Frontend UI, Mock Model Responses, Error Flows, Event Logging Plan, and Handoff Documentation)
Session Deliverable: Initialized Application Workspace & Interactive UI Shell

What does “good enough to ship” mean, and how do you test a probabilistic, multi-step system?
Core Hands-on Competencies

• Golden Dataset Engineering: Curating and formatting highly representative evaluation datasets, including defining system prompt inputs, expected outputs, explicitly unacceptable output responses, and precise alignment rubrics.
• LLM-as-Judge Automation: Configuring and tuning automated LLM-as-judge prompts, establishing scoring constraints, and calibrating the automated evaluator against human grading baselines to avoid judge bias.
• CI/CD Regression Deployment: Deploying automated evaluation runs into software delivery pipelines to catch prompt regressions and system drift before push commands execute (using Promptfoo and Braintrust).
• RAG Pipeline Evaluation: Implementing testing matrices to evaluate RAG configurations, specifically testing context retrieval quality, source material faithfulness, and hallucination containment.
• Agentic Trajectory Evals: Designing multi-step evaluation checkpoints that audit and score the entire operational path, tracking intermediate tool-use correctness rather than simply checking the final terminal answer string.
• Compliance & Tone Validation: Structuring automated semantic tests to verify corporate tone alignment, brand safety guidelines, and regulatory compliance constraints.

Additional topics that will be shortly reviewed:

• Understanding why traditional deterministic software QA models fail completely when testing non-deterministic, probabilistic AI behaviors.

Hands-on Workshop: Participants assemble a functional golden dataset and grading rubric, execute a live automated prompt evaluation using the Promptfoo CLI tool, and configure an automated multi-step trajectory path validation test.
Session Deliverable: Eval Suite — Golden Dataset, Evaluation Rubric, and Automated CI/CD Testing Gate.

How do PMs make the strategic calls on models, cost and margin, and what it actually takes to ship?
Core Hands-on Competencies

• Model Optimization Architecture: Evaluating trade-offs across commercial APIs vs. open-source weights, RAG architectures vs. fine-tuning, small task-specific models (SLMs) vs. large general foundation models (LLMs), latency-cost-quality optimization vectors, context windows, and dynamic model routing tables.
• Granular Cost-Bucket Modeling: Deconstructing the traditional COGS equation down into discrete Input, Output, and Retrieval cost buckets. Applying targeted engineering levers to maximize margins (e.g., prompt caching optimization to reduce input costs, strict max-token caps to minimize output costs, and semantic chunking to decrease retrieval costs).
• Agentic Financial Control: Building token multiplication projection models for multi-step agent behaviors, linking application cost paths directly back to the execution circuit-breaker caps.
• The 3× Value Heuristic: Applying financial stress testing to verify that an AI capability generates measurable business value at least three times greater than its direct compute cost, customizing the model against company gross margin targets and Customer Acquisition Cost (CAC) structures.
• Pricing & TCO Calculations: Building robust Total Cost of Ownership (TCO) matrices spanning costs per request, per active user, and per resolution; analyzing pricing frameworks across usage-based, subscription, hybrid, or internal corporate chargebacks.
• Cross-Functional AI Governance: Constructing a specialized RACI matrix mapping operational ownership across Product Management, Engineering, Data Science, Security, and Legal teams for long-term model maintenance, evals, guardrail logic, and cost allocation.

Additional topics that will be shortly reviewed:

• Build vs. Buy evaluation criteria, filtering marketing claims, managing enterprise AI
  procurement processes, navigating security and compliance reviews, and reviewing engineering delivery workflows (CI/CD pipelines, code testing, and auditing AI-generated production code).

Hands-on Workshop: Participants construct a live, parameter-driven TCO and COGS spreadsheet model for their feature, run the 3× financial value check, map out a lifecycle RACI governance framework, and compile a final deployment readiness plan.
Session Deliverable: AI Product Production Plan + Executive Decision Brief (comprising the detailed COGS breakdown, 3× value validation, and cross-functional lifecycle RACI).