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🤖 AI & Agentic AIBeginner LevelEvidence-Based Skill Profile

Hands-on Mastery in AI Product Development

Master the discipline of designing, scoping, architecting, testing, launching, and continuously improving real-world AI-powered products with your dedicated AI Agent coach.

40 Modules • 9 Production Projects • Dedicated AI Coach
40 Modules
AI FearFilter Faculty

What You Will Learn

Identify and frame high-leverage user problems uniquely suited for AI solutions.
Author production-grade AI PRDs specifying data, model architecture, latency SLAs, and fallback paths.
Design human-centered AI user interfaces with confidence scoring and human-in-the-loop workflows.
Rapidly build and evaluate functional AI prototypes against user task completion criteria.
Model AI unit economics, token costs, gross margins, and customer lifetime value.
Establish release criteria with canary rollouts, kill switches, and automated rollback strategies.
Implement product telemetry pipelines capturing implicit user feedback and fine-tuning triggers.

Curriculum & Weekly Roadmap

40 Structured Modules

Module 1: Introduction to AI Product Development

  • What is AI Product Development?
  • The Mindset of an AI Product Builder
  • The AI Product Lifecycle

Module 2: What Is an AI Product?

  • Defining the AI Product
  • Types of AI Products: Feature vs System vs Platform
  • AI as an Engine vs AI as a Feature

Module 3: AI Product vs Traditional Software Product

  • Deterministic Logic vs Probabilistic Behaviors
  • Managing Uncertainty, Hallucination, and Error Budgets
  • Continuous Improvement and Model Drift

Module 4: Identifying Real User Problems

  • Problem-First vs Technology-First Thinking
  • Uncovering High-Value Pain Points
  • Sizing the User Problem and TAM/SAM/SOM

Module 5: Problem Discovery and User Needs

  • User Discovery Interviews for AI Products
  • Mapping User Pain to Opportunity Spaces
  • Validating Problem Urgency and Willingness to Adopt

Module 6: AI Opportunity Identification

  • The AI Feasibility vs Impact Matrix
  • When AI is the Right Solution
  • When AI is the WRONG Solution

Module 7: Defining the AI Product Vision

  • Crafting an Inspiring AI Product Vision Statement
  • Strategic Positioning and Moats in AI Products
  • North Star Metric for AI Products

Module 8: User Personas and Use Cases

  • Creating Realistic AI User Personas
  • Core vs Edge Use Cases in AI Products
  • Establishing User Trust and Expectations

Module 9: User Journey and Product Experience

  • Mapping the AI-Powered User Journey
  • Critical Moments of Truth in AI UX
  • Designing Onboarding and Value Discovery

Module 10: AI Product Requirements

  • Anatomy of an AI Product Requirement
  • Functional vs Non-Functional AI Requirements
  • Defining Edge Cases and Error States in PRDs

Module 11: Writing AI Product Requirements (PRD)

  • Structure of a Production AI PRD
  • Specifying Prompts, Models, and Context in Requirements
  • Acceptance Criteria and Pass/Fail Thresholds

Module 12: AI Product Feature Prioritization

  • Frameworks for AI Prioritization: RICE vs Value/Complexity
  • Balancing Quality, Cost, and Speed
  • The AI Feature Backlog and Icebox

Module 13: MVP Thinking for AI Products

  • What is a Minimum Viable AI Product?
  • Wizard-of-Oz and Concierge AI Prototypes
  • Fast Prototyping without Heavy Infrastructure

Module 14: Designing AI-Powered User Experiences (AI UX)

  • Core Principles of AI UX Design
  • Designing for Latency: Streaming, Skeletons, and Progress
  • Affordances for Editable and Regenerable AI Outputs

Module 15: Human + AI Interaction (HITL)

  • Levels of AI Autonomy in Products
  • Human-in-the-Loop Approval Gates
  • Building Mixed-Initiative Workflows

Module 16: AI Product Architecture Fundamentals

  • End-to-End AI Product Architecture
  • Decoupling Product Logic from Model Providers
  • Latency Budgets and Asynchronous Task Queues

Module 17: LLMs, Models and AI APIs

  • Commercial APIs vs Open Source Models
  • Choosing the Right Model for the Use Case
  • Managing Rate Limits, Quotas, and Retries

Module 18: Prompt Design for AI Products

  • System Prompts as Product Logic
  • Structured Outputs and JSON Mode
  • Dynamic Context Injection and Variables

Module 19: AI Agents and Tool Calling in Products

  • When Products Need AI Agents
  • Designing Safe Product Tools
  • Agent Memory and Context Management

Module 20: RAG and Knowledge-Based AI Products

  • Architecture of a RAG Product
  • Solving Information Retrieval Pain for Users
  • Citation, Source Attribution, and Verification

Module 21: AI Automation in Products

  • Workflow Automation vs Interactive Chat
  • Trigger-Action Architectures
  • Resilient Error Handling in AI Automations

Module 22: Data Flow in AI Products

  • User Input to Tokenized Representation
  • Data Pipelines for Dynamic Grounding
  • Managing State, Sessions, and Ephemeral Context

Module 23: Building an AI Product Prototype

  • Prototyping Stack: Next.js, FastAPI, and AI SDKs
  • Wireframing AI Interaction States
  • Conducting 5-User Usability Tests on AI Prototypes

Module 24: AI Product Backend and APIs

  • Designing REST & Streaming Endpoints for AI
  • Authentication, Multi-Tenancy, and User Isolation
  • Token Metering and Cost Accounting per Tenant

Module 25: AI Product Frontend Experience

  • Rendering Streaming Markdown and Code Blocks
  • Feedback UI: Thumbs Up, Down, and Inline Correction
  • Keyboard Shortcuts and Power-User Affordances

Module 26: AI Evaluation and Quality in Products

  • Product Quality vs Model Benchmarks
  • Designing Domain-Specific Evaluation Rubrics
  • LLM-as-a-Judge for Automated Product Testing

Module 27: AI Testing and Reliability

  • Building Regression Suites for AI Products
  • Handling Non-Determinism in Automated Tests
  • Stress-Testing Edge Cases and Incomplete Inputs

Module 28: AI Safety, Guardrails and Responsible Design

  • Input & Output Guardrails
  • Preventing Prompt Injections and System Prompt Leakage
  • Responsible AI Principles: Transparency, Privacy, and Control

Module 29: AI Product Analytics

  • Instrumenting AI User Events
  • Measuring Implicit vs Explicit User Feedback
  • Funnel Analytics for AI Products

Module 30: Measuring AI Product Success

  • Key Product Metrics: Time-to-Value & Task Completion
  • AI ROI & Value Quantification
  • Retention, Daily Active Usage, and Feature Stickiness

Module 31: User Feedback and AI Product Improvement

  • Closing the Feedback Loop
  • Building the Negative Test Case Flywheel
  • Continuous Prompt and RAG Tuning

Module 32: AI Product Cost and Performance

  • Unit Economics: Cost per User & Cost per Query
  • Optimizing Token Usage: Prompt Compression & Caching
  • Latency Optimization: TTFT, ITL, and Model Distillation

Module 33: AI Product Security and Privacy

  • Zero Data Retention (ZDR) and Enterprise Agreements
  • PII Redaction and Anonymization Pipelines
  • SOC2, GDPR, and Enterprise AI Compliance

Module 34: Launching an AI Product

  • Phased Launch Strategy: Alpha, Beta, and General Availability
  • Setting User Expectations and Guarding Against Hype
  • Launch Runbook: Rollback Plans and Circuit Breakers

Module 35: Production Monitoring and Telemetry

  • Real-Time Observability: Tracing and Latency Histograms
  • Detecting Semantic Drift and Output Degradation
  • Error Budgeting and SLA Alerts

Module 36: Iterating and Improving an AI Product

  • The AI Product Flywheel
  • A/B Testing Prompts and Models in Production
  • Model Migration Playbook

Module 37: AI Product Roadmaps

  • Horizon Planning for AI Products: Immediate, Medium, Long Term
  • Navigating Rapid Foundation Model Advancements
  • Communicating AI Capabilities to Stakeholders and Customers

Module 38: AI Product Growth and Scaling

  • Growth Loops in AI Products
  • Scaling Infrastructure from 100 to 100,000 Daily Queries
  • Expansion from Single-User Tool to Enterprise Workflow

Module 39: Enterprise AI Product Governance and Compliance

  • Enterprise Readiness Checklist
  • AI Governance Frameworks: Risk Classification and Oversight
  • Human Recourse, Explainability, and Dispute Resolution

Module 40: Capstone: Build a Production-Style AI Product

  • Module 40 Capstone Platform Architecture
  • Complete Product Dossier and Implementation
  • Final Automated AI Agent Assessment and Verification

Who This Course Is For

Aspiring AI Product Managers, Technical Founders, AI Engineers, Software Developers, and CS Students aiming to design, build, test, and launch production AI products.

Key Skills Developed:

AI Problem Framing, Feasibility & Opportunity DiscoveryAI PRD Authoring, Scoping & User Journey MappingHuman-in-the-Loop AI UX, Confidence & Fallback DesignAI Capabilities Selection & Rapid PrototypingProduct Evaluation Scorecards, Safety Guardrails & TrustToken Unit Economics, Latency & Throughput SLAsRelease Engineering, Alpha/Beta & Canary Rollout StrategiesProduct Analytics, Continuous Telemetry & Data FlywheelsProduction AI Product Launch Platform Capstone

Course Faculty & Development

AI FearFilter Faculty

AI Product & Engineering Team

AI FearFilter Academy

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