AI-Assisted Product Execution & Feasibility for PMs
Bridge Product Management and AI engineering: master attention budgets, prompt architectures, 4-axis data readiness audits, capability-task fit matrices, and executive BLUF feasibility briefs.
AI PM Execution Sandbox
Context & Feasibility Sandbox
Prunes fluffy personas, focusing token budget on high-variance few-shot examples to prevent lost-in-the-middle.
3 Modules & 26 In-Depth Lessons
From Prompting & Attention Budget mastery and AI Bet portfolio prioritization to writing Technical Requirements for RAG architectures.
Shift from "Prompt Writing" to Attention Budget Management
Shift from magical prompt phrasing to managing a finite Attention Budget, curating the highest-signal token set for every inference call.
Value Personas by Behavioral Function, Not Decorative Titles
Value personas by concrete behavioral function instead of decorative titles, and distinguish persona's opposite effects on Precision vs. Generative tasks.
Curate Context and Streamline PRDs with the "What Does This Token Buy?" Rule
Eliminate the 4 context-stuffing traps that dilute model signals, and streamline PRDs and background docs to their highest-signal behavioral drivers.
Curate Diverse Few-Shot Examples, Don't List Them All
Curate 2–4 high-variance few-shot examples instead of listing dozens of repetitive variants, maximizing the ROI of few-shot learning.
Match Prompt Structure to PM Task Type and Decompose Hybrid Pipelines
Architect prompts for Precision vs. Generative tasks, and decompose hybrid workflows into independent 2-step pipelines instead of one monolithic prompt.
Work with Reasoning Models and Lock Down Output Format
Describe Definition of Done for modern Reasoning Models instead of forcing step-by-step chains, and lock output structure to eliminate formatting variance.
Explain Constraint Rationale Instead of Shouting in ALL CAPS
Engineer robust constraints with rationale instead of bare ALL CAPS commands, and separate hard execution constraints from rationale-guided directives.
Treat Prompts as Testable Hypotheses and Apply the Production Checklist
Establish a 3-tier prompt testing workflow, resolve failure root causes instead of brittle patching, and apply the 8-point production checklist before release.
Everything You Need to Know
How does this course differ from AI Literacy?
AI Literacy establishes mental models (Tokens, Models, RAG, Evals). AI Product Management dives deep into execution & feasibility: attention budget engineering, 4-axis data readiness audits, capability-task fit mapping, non-linear cost modeling, executive BLUF feasibility briefs, and writing precise technical requirements for RAG architectures (chunking, retrieval quality, grounding, data freshness).
Which case studies are used throughout the lessons?
The first 15 lessons (Modules 1-2) are anchored in 2 realistic production platforms: FreelanceFlow (Release Notes generation, ticket synthesis) and FinTrack Logistics/Fintech (route optimization, invoice OCR, and fraud triage). Module 3 (RAG) uses its own running example: a refund chatbot for an e-commerce support team.
What concrete PM deliverables will I produce?
You will complete 4 production-grade artifacts: System Prompt & Attention Budget Spec, 4-Axis Data Readiness Audit, Capability-Task Fit Matrix with Guardrails, and a 2-page BLUF Feasibility Brief ready for leadership review.
Do I need coding experience or technical background?
No coding is required. The curriculum is specifically designed for Technical PMs, AI PMs, and Product Leaders, focusing on architectural feasibility, system trade-offs, and strategic decision making.
Ready to Master AI Product Execution & Feasibility?
Start immediately with Lesson 1: Managing Attention Budget & Curating High-Signal Tokens on FreelanceFlow.