LIVE AI Product Manager Bootcamp: Scope, Orchestrate & Ship

Session 3 - Stakeholder Management + Personal Branding

Communication as a timeless PM skill • The AER Framework • Listening ratio & self-awareness • Stakeholder mapping & power-interest grid • How to build your personal brand on LinkedIn

Output: User Research & Problem Statement Artifact

Session 4 - AI Problem Discovery & User Research

Why AI projects fail • Perceived vs real problem • The AI PM Discovery Framework • Mom Test & JTBD interviews • Persona, empathy map & 5 Whys

Output: AI Discovery Framework & Stakeholder Matrix

Session 5 - Prompting, Context & RAG for AI PMs

The RCTF prompting framework • Prompting techniques: zero-, one-, and few-shot • System prompts • Embeddings • Vector databases & semantic search • RAG: indexing and retrieval • What is RAG & why RAG (limitations of prompting alone) • How RAG works • Fine-tuning vs Prompting vs RAG • RAG for PMs: use cases, trade-offs & when to reach for it

Output: RCTF Prompt Engineering Library

Session 6 - Vibe Coding (Building a RAG Application)

Live RAG chatbot build • PRD-first approach • How RAG works • Tech stack: Groq, LangChain, Streamlit, an IDE, Claude Code/Cowork, and Git/GitHub • Claude Design

Output: Working Live RAG Chatbot Prototype

Session 7 - AI Agents

What is an AI agent • The ReAct loop • Multi-agent systems • Agents vs workflows • Multimodal AI • Tool calling (MCP covered if possible) • Memory • LangChain vibe coding project activity

Output: AI Agent build

Session 8 - Cost Estimation for RAG & Gen AI Systems + Pricing & Unit Economics

Token fundamentals • OpenAI pricing models • Inference cost • Embedding cost • ROI vs human support cost • Pricing models • Unit economics • Hands-on cost estimation activity

Output: API Token Cost & ROI Financial Model

Session 9 - AI Metrics & AI Evals Foundations

AI metrics - what to measure and why • Product, Business & AI metrics - how they differ • AI Eval Stack - what “good enough to ship” means • Human Evaluation vs LLM-as-a-Judge • Hands-on activity: evaluating a sample AI output

Output: E2E LLM Evaluation Suite & Judge Rubric

Session 10 - AI Evals Part 2 & Guardrails

Precision, Recall & F1, AUC-ROC: reading metrics to make a trade-off call • What guardrails are & why PMs need them • Hallucination mitigation • Safety guardrails: defining failure modes to block

Output: Production AI Guardrail Specs

Session 11 - Product & Business Metrics + Capstone Launch

Product metrics (engagement, retention, adoption) • Business metrics (revenue, ROI, cost-to-serve) • North Star Metric • Model drift, context drift & data drift • Micro-activity: metrics mapping exercise • Capstone project launch

Output: Product & Business Metrics + Capstone Kickoff

Session 12 - AI Opportunity Mapping

AI opportunity mapping • The Miro process board • 33A AI cards • Workflow mapping for AI use cases • Guardrails & human-in-the-loop

Output: AI Opportunity Miro Map

Session 13 - AI PRD Architecture

Product Development Lifecycle • Why PRDs exist • BRD vs PRD vs FRD • Traditional PRD anatomy • Where AI PRDs differ

Output: Production AI PRD Spec

Session 14 - AI UX Design

AI UX principles - designing for trust, transparency & control • Explainability & confidence: helping users understand AI decisions • The HAX Toolkit - Microsoft's Human-AI Interaction guidelines, applied • Common AI UX failure patterns (over-automation, invisible errors, lack of feedback) • Hands-on: running a HAX Toolkit evaluation on a real product

Output: HAX AI UX & Trust Guidelines

Session 15 - Responsible AI + AI Strategy (GTM, Moats & Roadmap)

Responsible AI as a strategic input - bias, fairness & transparency risk in your roadmap • The EU AI Act as a GTM constraint, not a side topic • Building responsible-AI checkpoints into GTM strategy • Identifying moats for an existing business - and where responsible-AI practice becomes the moat • Writing AI roadmaps with guardrails built in from the start

Output: EU AI Act Risk & Compliance Roadmap

Session 16 - Feasibility/Impact Analysis

Prioritising AI use cases using feasibility/impact analysis • Technical feasibility vs business impact - the 2x2 framework • Scoring criteria: data readiness, cost, risk, and expected value • Live session: running feasibility/impact analysis with an actual client • Translating the analysis into a go/no-go recommendation

Output: Live Client Feasibility & Business Impact Matrix

Session 17 - Data Strategy and Readiness

Why data strategy matters • PM vs data engineer responsibilities • Types of context • Data procurement & readiness • Golden data & storage

Output: Data Readiness & Cost vs Latency Trade-off Sheet

Session 18 - Designing AI Systems

AI system design for PMs (technical, hands-on) • Automation vs ML vs GenAI vs Agent - decision framework • Real-life case studies from AtliQ's PM & AI team • Production trade-offs for AI PMs • Build vs Buy vs Partner • Reading model benchmarks • Multimodal AI • Cost vs Quality vs Latency

Output: Production AI Architecture Diagram

Session 19- Mock Interviews + Career Readiness + The Complete AI PM Recap: From Fundamentals to First 90 Days

Live mock interview practice • Practising case studies related to PM interviews • Career readiness for AI PM roles • How to write an ATS-friendly resume for AI PMs • The Complete AI PM Recap: From Fundamentals to First 90 Days (~45 mins) • Open Q&A

Output: Live Mock Interview & Feedback

Session 20 - Demo Day

Final capstone presentations (top 3 submissions) • Expert panel review • Product feedback • Graduation & next steps

Output: Capstone Project Deck & Demo Day Presentation