Become an AI product builder

Master LLM mechanics to design, evaluate, and ship production-grade AI systems with high reliability.

ORCHESTRATORGOAL

Architecture Standards & Foundational Research

Google DeepMind
Anthropic
OpenAI
Stanford HAI

How Product Management Has Fundamentally Shifted

Building AI products is shifting from deterministic logic to probabilistic reasoning and autonomous systems.

SYSTEM NATURE
AI Literacy Lessons 01–04
Traditional PM

Deterministic Logic

Writing PRDs with rigid IF-ELSE rules, expecting static 1:1 input-output guarantees.

AI-Era PM

Probabilistic Management

Analyzing token probability distribution, tuning temperature, and designing fault-tolerant UX.

CONTEXT & KNOWLEDGE
AI Literacy Lessons 05–08
Traditional PM

Hardcoded Static Knowledge

Feeding static FAQ documents into wikis, relying on naive keyword lookup.

AI-Era PM

Context Budgeting & Grounding

Optimizing the context window, enforcing token budgets, and eliminating hallucinations via grounding.

TOOLING & EXECUTION
AI Literacy Lessons 11–12 & AI Agents Day 2
Traditional PM

Static Request-Response APIs

Building button-triggered single API endpoints following predetermined hardcoded flows.

AI-Era PM

MCP Tool Protocol & Agent Loops

Standardizing Tool Schemas for autonomous planning, intent dispatch, and parallel tool calling.

Structured Curriculum Pathways

From foundational mental models and product execution to advanced autonomous agent systems.

View All Pathways
FOUNDATIONFree

AI Literacy: Practical Foundations

Master foundation models, prompt systems, RAG, and evals through a PM decision framework with EcoCart.

Probability Mechanics & Context Windows

RAG Systems & Skill Authoring

Quantitative Evals & Dual Guardrails

Deliverable: AI Feature PRD & Pass^k Benchmark.
ADVANCED

AI Agents for PM: Autonomous Systems

Master autonomous AI agent systems: 33 research diagrams, local MCP prototypes, eval suites, and Capstone.

5 Architectural Layers & MCP Standard

Long-Term Memory & Security Triad

Launch / No-Go Decision Gate

Deliverable: Agent Architecture & Safety Evals.
APPLIED PM

AI Product Management: Execution & Feasibility

Context engineering, 4-axis Data Readiness audits, Capability-Task Fit matrices, and executive BLUF briefs.

Attention Budgeting & Prompt Systems

4-Axis Data Readiness & Task-Fit Matrix

BLUF Feasibility Brief & Calibrated ICE

Deliverable: BLUF Feasibility Brief & ICE Risk Matrix.

From Visual Mechanics to Production Impact

Master model mechanics, practice on EcoCart (50k orders/day), and evaluate with technical eval suites.

02. FLAGSHIP CASE STUDY
EcoCart · 50k Orders/ngày

EcoCart Platform & 4 Build Milestones

Throughout the course, act as PM designing an autonomous return & warranty AI agent, integrating logistics APIs, and managing financial risk.

Intent Classification & Triage
STEP 1: TRIAGE
# Customer Input Query:

“The sweater has loose stitching on the armpit, I want an exchange.”

# Agent Decision:

Valid 14-day warranty return (Delivered 4 days ago · Manufacturing defect)

Urgency TierTier 4/10
Scope CheckVALID RETURN
Resolution SLA< 45s SLA
Key Deliverable: Production-ready PRD & Model Context Protocol Tool Schema.
01. VISUAL MECHANICS
Bài 01–04

Token Sampling & Temperature

Tune temperature based on response requirements.

Temperature (T):
“Paris” (Thủ đô)88.4%
“thành phố ánh sáng”8.1%
OUTCOME:Calibrate parameters per business case
03. EVALS & DRILLS
Bài 11–14

Quantitative Evals & Release Gate

Build a 500-case benchmark and measure Pass@k.

Quality Gate:
Pass@1 Release Metric98.6% (493/500)
RELEASE SIGN-OFF
PROD READY ✓
OUTCOME:Quantitative Launch vs No-Go criteria

Every Millisecond of an Agent's Decision Loop

Beyond the black box - observe how autonomous agents parse intent, invoke MCP tools, and enforce safety guardrails.

Decision Stepper
Fully Autonomous (Level 1)
11. Intent & Policy Triage
+45ms

Order eligible for return within 14-day policy window

22. MCP Tool Protocol Dispatch
+120ms

Call logistics tool to generate return waybill

Tool:logistics.generate_return_waybill
33. Output Verification & Sign-off
+80ms

Initiate $48.50 Stripe refund and send QR return code

Status:PASSED
Action DecisionTự động hoàn toàn
APPROVE_REFUND
Refund Amount
$48.50 USD (Stripe API)
Generated Waybill
WB-9924-GHN (via MCP Tool)
Fraud Risk Score
0.02 (Safe Threshold < 0.15)An toàn
Latency p75245ms
Token Cost$0.0018
Pass Rate98.4%

3 Core AI Product Principles

Three technical pillars to transition from ad-hoc experimentation to deterministic production systems.

01. MENTAL MODELS

Probability over Certainty

Calibrate model confidence thresholds and design dual-path automated routing.

CONFIDENCE METERP(y|x) = 0.88
p ≥ 0.80Auto-Run
p < 0.80Human Gate
02. QUANTITATIVE EVALS

Quantitative Pass^k Metric

Replace manual vibe checks with rigorous automated Pass@k benchmark suites.

RELIABILITY CURVEPass@5: 99.9%
k=1 (98.6%)k=5 (99.9%)
Release Target:Pass@1 ≥ 98.5% ✓
03. DEFENSE IN DEPTH

Two-Layer Guardrails

Enforce multi-tier defense: input prompt firewall and strict output schema validation.

SECURITY TRIAD2-LAYER ACTIVE
1Input Firewall
Chặn Injection
2Output Verifier
Hậu kiểm Schema
Risk Block Rate:100% Guarded ✓

Ready to Level Up Your AI Product Mindset?

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