Build AI Products That Make the Right Decisions and Scale Them to Production

Learn how top Product and TPM teams design, evaluate, and launch agentic AI products, from no-code prototypes to production-ready systems, guided by FAANG+ PMs and AI architects.

This program trains you to own AI product decisions end to end: architecture, metrics, risk, ROI, and rollout.

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Program Overview

Who It Is Built For

Product Managers owning AI and agentic product decisions end to end
PMs transitioning into AI first and agentic product roles
PMs accountable for measurable impact, ROI, and scale

Program Duration

15 weeks of structured AI product training
Progresses from agentic AI foundations to launch and scale
Designed to fit alongside a full time PM role

Live Learning

80+ hours of live instruction
Focused on AI product decisions, system boundaries, and metrics
Led by FAANG+ Product Managers and AI leaders

Hands On Product Work

15+ hours of guided AI product initiatives
Built using no code and low code tools
Grounded in real product decision scenarios

Instructors

FAANG+ Product Managers and AI architects
Leaders who have launched and scaled AI products in production
Guidance based on real product accountability

Projects

3 live guided AI product projects
2 capstone level product initiatives
Emphasis on end to end product ownership

AI Product Decision Frameworks

Make agentic AI decisions using metrics, cost, and risk
Evaluate build versus buy and model selection choices
Define success criteria and launch readiness

Exclusive Agentic AI Interview Preparation for PMs

The only PM program with integrated agentic AI interview prep
Practice AI product cases, metrics reasoning, and system tradeoffs
Prepare for senior PM interviews focused on AI product ownership

Average package for alumni

$ 112275

Careers transformed

0 K+

Average ROI on course price

0 x

20+ Tools & Tech You’ll Learn

Why PMs Choose This Applied Agentic AI Program

Built for Real AI Products

Work on real agentic AI product initiatives, not demos
Focus on use case selection, system boundaries, and launch readiness
Build products the way AI products are actually shipped

What PMs Actually Do at Work

Learn agentic AI concepts tied to real product decisions
Focus on problem framing, prioritization, and roadmap tradeoffs
Apply patterns directly to active product initiatives

Own AI Product Decisions End to End

Understand how agentic AI changes product discovery and delivery
Gain skills to define architecture choices, metrics, and success criteria
Own ROI, risk, and scale decisions, not just requirements

Learn from PMs Building It Today

Guided by 700+ FAANG+ Product Managers and AI leaders
Learn directly from professionals launching AI products in production
Get insights grounded in real product ownership

Exclusive Agentic AI Interview Prep for PMs

The only PM program with integrated agentic AI interview preparation
Learn to defend product decisions, metrics, and system tradeoffs
Practice senior PM interviews focused on AI product ownership

Results PMs Trust

Trusted by thousands of professionals globally
NPS of 55 and learner rating of 4.75 plus
Outcomes driven by applied learning and real product work

This program trains PMs to own AI product decisions end to end, the way AI products are built in 2026.

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Detailed Curriculum: Applied Agentic AI for Product Managers

Applied Agentic AI Core for PMs

Foundations and No-Code Setup

Core concepts of LLMs, agents, and multi-agent systems
No-code tools such as LangFlow, Relevance AI, and Flowise
AI system layers including data, model, agent, and orchestration
How prompts, retrievers, and agents connect in real products

Outcome: Build strong intuition for agentic AI products and ship a first working agent using no-code tools.

Agentic Fundamentals for Product Decisions

Reflex, goal-based, utility, and LLM-driven agents
Memory design including buffer, summary, and vector memory
Prompting, tool usage, and logic control
Cost, latency, reliability, and determinism tradeoffs

Outcome: Understand how agent behavior impacts UX, cost, and product reliability.

RAG Knowledge Agents

Embeddings and chunking strategies from a product lens
Vector database concepts and retrieval pipelines
Grounded response generation and evaluation basics

Outcome: Design reliable RAG-based products that reduce hallucinations and improve trust.

Multi-Agent Product Workflows

Task decomposition into agent roles
Planner, executor, and critic collaboration patterns
Debugging and coordination visibility for PMs

Outcome: Define multi-agent workflows that support complex product use cases.

Conversational and Multimodal Product Experiences

Memory retention strategies for long-running product agents
Conversational UX, tone, and persona design
Voice and multimodal interfaces for user-facing AI products

Outcome: Design stateful conversational AI experiences with voice and multimodal inputs.

AI Product Architecture

RAG vs agents vs pipelines for different product needs
Build vs buy decisions
Data flow and system diagrams
Latency, throughput, and cost constraints

Outcome: Make confident architecture choices for AI features and platforms.

Evaluation, Safety, and ROI

Product-level evaluation concepts
Token cost modeling and unit economics
Accuracy vs satisfaction metrics
Governance and safety fundamentals

Outcome: Define KPIs, measure ROI, and assess safety readiness for AI launches.

Personalization and Fine-Tuning (No-Code)

Fine-tuning concepts including LoRA and PEFT
Dataset curation for personalization
Comparing prompting, RAG, and fine-tuning outcomes

Outcome: Decide when personalization and fine-tuning are justified from a product ROI lens.

AI Product Initiative (Capstone)

No-code orchestration using LangFlow, Relevance AI, and automation tools
End-to-end AI product initiative
Product strategy deck with KPIs and ROI rationale

Outcome: Deliver a complete AI product proposal ready for stakeholder and leadership review.

Agentic AI Product Design and Interview Preparation for PMs

AI Use Case Framing and Metrics

Problem framing and AI fit assessment
Deterministic vs AI vs agentic decisions
Success metrics, guardrails, and MVP scope

Outcome: Confidently frame AI product problems and justify agentic decisions in PM interviews.

Architecture & Workflows

End-to-end AI product architecture
Orchestration patterns and integrations
Permissions and non-functional constraints

Outcome: Clearly explain AI system design and tradeoffs expected in senior PM interviews.

Data and Retrieval Strategy

Approved data sources and retrieval behavior
Data quality, freshness, and ownership
Traceability and versioning for trust

Outcome: Defend data and trust decisions in AI product discussions.

Evaluation and Monitoring

Offline evaluation strategies
Online monitoring and feedback loops
Iteration, release gating, and rollback decisions

Outcome: Explain how AI products are evaluated, monitored, and improved after launch.

Risk and Governance

Risk tiers and human-in-the-loop design
Security, privacy, and prompt misuse controls
Responsible AI alignment

Outcome: Handle AI risk and governance questions with product-level clarity.

Launch and Scale

Rollout strategy and change management
Cost, ROI, and operating constraints
Build vs buy decisions and pilot de-risking

Outcome: Confidently discuss AI product launch, scale, and ROI tradeoffs.

Specialized Sessions

Prompt Engineering and No Code Tools

Product Design and User Experience with AI & ML Products

Defining AI-Powered Product Requirements

AI Product Execution and Implementation

Detailed Curriculum: Applied Agentic AI for Product Managers

Applied Agentic AI Core for PMs

Foundations and No-Code Setup

Core concepts of LLMs, agents, and multi-agent systems
No-code tools such as LangFlow, Relevance AI, and Flowise
AI system layers including data, model, agent, and orchestration
How prompts, retrievers, and agents connect in real products

Outcome: Build strong intuition for agentic AI products and ship a first working agent using no-code tools.

Agentic Fundamentals for Product Decisions

Reflex, goal-based, utility, and LLM-driven agents
Memory design including buffer, summary, and vector memory
Prompting, tool usage, and logic control
Cost, latency, reliability, and determinism tradeoffs

Outcome: Understand how agent behavior impacts UX, cost, and product reliability.

RAG Knowledge Agents

Embeddings and chunking strategies from a product lens
Vector database concepts and retrieval pipelines
Grounded response generation and evaluation basics

Outcome: Design reliable RAG-based products that reduce hallucinations and improve trust.

Multi-Agent Product Workflows

Task decomposition into agent roles
Planner, executor, and critic collaboration patterns
Debugging and coordination visibility for PMs

Outcome: Define multi-agent workflows that support complex product use cases.

Conversational and Multimodal Product Experiences

Memory retention strategies for long-running product agents
Conversational UX, tone, and persona design
Voice and multimodal interfaces for user-facing AI products

Outcome: Design stateful conversational AI experiences with voice and multimodal inputs.

AI Product Architecture

RAG vs agents vs pipelines for different product needs
Build vs buy decisions
Data flow and system diagrams
Latency, throughput, and cost constraints

Outcome: Make confident architecture choices for AI features and platforms.

Evaluation, Safety, and ROI

Product-level evaluation concepts
Token cost modeling and unit economics
Accuracy vs satisfaction metrics
Governance and safety fundamentals

Outcome: Define KPIs, measure ROI, and assess safety readiness for AI launches.

Personalization and Fine-Tuning (No-Code)

Fine-tuning concepts including LoRA and PEFT
Dataset curation for personalization
Comparing prompting, RAG, and fine-tuning outcomes

Outcome: Decide when personalization and fine-tuning are justified from a product ROI lens.

AI Product Initiative (Capstone)

No-code orchestration using LangFlow, Relevance AI, and automation tools
End-to-end AI product initiative
Product strategy deck with KPIs and ROI rationale

Outcome: Deliver a complete AI product proposal ready for stakeholder and leadership review.

Agentic AI Product Design and Interview Preparation for PMs

AI Use Case Framing and Metrics

Problem framing and AI fit assessment
Deterministic vs AI vs agentic decisions
Success metrics, guardrails, and MVP scope

Outcome: Confidently frame AI product problems and justify agentic decisions in PM interviews.

Architecture & Workflows

End-to-end AI product architecture
Orchestration patterns and integrations
Permissions and non-functional constraints

Outcome: Clearly explain AI system design and tradeoffs expected in senior PM interviews.

Data and Retrieval Strategy

Approved data sources and retrieval behavior
Data quality, freshness, and ownership
Traceability and versioning for trust

Outcome: Defend data and trust decisions in AI product discussions.

Evaluation and Monitoring

Offline evaluation strategies
Online monitoring and feedback loops
Iteration, release gating, and rollback decisions

Outcome: Explain how AI products are evaluated, monitored, and improved after launch.

Risk and Governance

Risk tiers and human-in-the-loop design
Security, privacy, and prompt misuse controls
Responsible AI alignment

Outcome: Handle AI risk and governance questions with product-level clarity.

Launch and Scale

Rollout strategy and change management
Cost, ROI, and operating constraints
Build vs buy decisions and pilot de-risking

Outcome: Confidently discuss AI product launch, scale, and ROI tradeoffs.

Specialized Sessions

Prompt Engineering and No Code Tools

Product Design and User Experience with AI & ML Products

Defining AI-Powered Product Requirements

AI Product Execution and Implementation

Live Guided Projects

Reflex FAQ Agent

Build a hybrid FAQ agent combining deterministic reflex logic with LLM-based reasoning, including decision-logic visualization to understand cost, latency, and reliability tradeoffs.

Internal Knowledge Assistant

Develop a RAG-based assistant that ingests PDFs and Notion documents, retrieves grounded answers, and uses evaluation logs to track retrieval relevance and response quality

AI Research Team

Design a multi-agent research automation flow where specialized agents (researcher → analyst → critic) collaborate to gather, analyze, and validate information.

Voice Feedback Agent

Create a voice-enabled customer feedback analyzer with persistent memory blocks, focusing on conversational flow, memory retention, and multimodal interaction.

AI System Blueprint

Produce a complete system architecture for an AI feature (e.g., support bot or analyzer), covering data flow, orchestration, integrations, and enterprise constraints.

AI Evaluation Dashboard

Design a KPI dashboard that tracks model accuracy, cost per user, and response quality to support launch readiness and iteration decisions.

Personalized Product Advisor

Build a fine-tuned Q&A model integrated into LangFlow and create a comparison report evaluating prompting vs RAG vs fine-tuning outcomes.

Live Guided Projects

Reflex FAQ Agent

Build a hybrid FAQ agent combining deterministic reflex logic with LLM-based reasoning, including decision-logic visualization to understand cost, latency, and reliability tradeoffs.

Internal Knowledge Assistant

Develop a RAG-based assistant that ingests PDFs and Notion documents, retrieves grounded answers, and uses evaluation logs to track retrieval relevance and response quality

AI Research Team

Design a multi-agent research automation flow where specialized agents (researcher → analyst → critic) collaborate to gather, analyze, and validate information.

Voice Feedback Agent

Create a voice-enabled customer feedback analyzer with persistent memory blocks, focusing on conversational flow, memory retention, and multimodal interaction.

AI System Blueprint

Produce a complete system architecture for an AI feature (e.g., support bot or analyzer), covering data flow, orchestration, integrations, and enterprise constraints.

AI Evaluation Dashboard

Design a KPI dashboard that tracks model accuracy, cost per user, and response quality to support launch readiness and iteration decisions.

Personalized Product Advisor

Build a fine-tuned Q&A model integrated into LangFlow and create a comparison report evaluating prompting vs RAG vs fine-tuning outcomes.

Capstone Projects

AI-Powered Sales Lead Optimization

Build an an AI-powered sales assistant that automates lead qualification, recommends furniture using real-time inventory, and delivers weekly sales insights for Oak & Ember Interiors. Using agentic workflows and grounded LLMs, it replaces manual processes with an accurate, scalable system that completes with human review, privacy guardrails, and measurable efficiency gains.

AI-Powered PRD Generator for Product Teams

Build PRD Genie—an agentic AI assistant that turns meeting transcripts and notes into structured PRDs, epics, and user stories. Grounded in real inputs and designed with human review, it cuts documentation time and accelerates product delivery at NeuronForge.

Bring Your Own Project [BYOP]

Work on personal or professional projects of your choice. BYOP offers mentorship, structured guidance, and feedback to ensure projects are aligned with industry standards and best practices. It fosters creativity, innovation, and real-world problem-solving, enabling you to build impactful solutions. You will receive guidance on selecting the right tools and frameworks based on project requirements.

Capstone Projects

AI-Powered Sales Lead Optimization

AI-Powered PRD Generator for Product Teams

Bring Your Own Project [BYOP]

FAANG+ Instructors to Train You

Get mentored by AI/ML leaders who are driving Agentic AI innovation at top global companies.

Ralph Blanes

Research Software Engineer

Rishabh Misra

Lead ML Engineer & Researcher

Behnoosh Parsa

Applied Scientist - GenAI

Ram Vegiraju

ML Architect @ AWS

Shruti Goli

Senior Product Manager, ex-Microsoft

Grace Burgio,

Product Manager, Meta - AR/VR Reality Labs

Praveen Kumar Kashimsetty

Director of Product, Rafay, ex-Meta

Manoj Panikkar

Product Lead (AI/ML), Uber, ex-Walmart

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The IK Experience: What Our Alumni Are Saying

Our engineers land high-paying and rewarding offers from the biggest tech companies, including Facebook, Google, Microsoft, Apple, Amazon, Tesla, and Netflix.

Venkatesh Babu

Senior Director - ISV/SolEx Technical Partner Management

I highly recommend the Applied GenAI course by Interview Kickstart. The PM path was incredibly well-organized, reshaping my thinking on how to leverage Generative AI in product management. The hands-on approach, insightful curriculum, and experienced instructors made it an outstanding learning experience!

Amit Vaswani

Lead Software Engineer, Highnote

I recently completed the Applied Gen AI course at Interview Kickstart, and I couldn't be more impressed. The course was well-structured into modules, making complex concepts easier to digest. For someone without a strong programming background, I appreciated the beginner Python class they offer for non-programmers—it helped build a solid foundation. The instructors are fantastic and always go the extra mile to ensure every question is answered. They are patient and don’t rush through the material, often allowing classes to run over to ensure everyone fully grasps the concepts. One of my favorite features is the 'Expert Connect,' where you can have 1-on-1 sessions with instructors to clear any doubts. Overall, Interview Kickstart provided an exceptional learning experience, and I highly recommend it to anyone looking to uplevel in their career.

Abhishek Singh

Senior Data Analyst, Jeavio

IK is a GOD-Send to me. If you are looking to upskill or transition in your career, IK is the place to be. I joined Data Science Switchup in November-23 and I am loving the 360 degree experience which IK provides. The instructors are professionals who are currently working in the tier-1 companies so the classes have loads of real-life snippets of their experience which adds true value to students like us. The mock sessions and technical sessions helped me immensely to understand the topic at a deeper level. The Ops Team & Success Coaches are superstars who act like a true friend when you need any sort of assistance.

Dwaraknath Bakshi

Senior Director of Engineering, Freshworks

IK has been an integral part of my life for the last seven years. Since being part of some of the early batches to most recently pursuing their ML track I have done several courses with them. What amazes me is the approachability of the staff, the proactive support team and professionalism. The material is amazing and profound. Omkar and Niloy to name a few they have amazing faculty who are very knowledgeable and are also great teachers. I can keep raving about IK. It’s definitely a boon to software engineers.

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Select a course based on your goals

Agentic AI

Learn to build AI agents to automate your repetitive workflows

Switch to AI/ML

Upskill yourself with AI and Machine learning skills

Interview Prep

Prepare for the toughest interviews with FAANG+ mentorship

FAQs

What is Agentic AI, and how is it different from traditional AI?

Agentic AI refers to AI systems that act as autonomous “agents” capable of reasoning, planning, and executing tasks independently or in collaboration with other agents. Unlike rule-based systems, these agents can orchestrate complex workflows using LLMs, adapt over time, and integrate with real-world tools—making them more powerful and practical for business and tech applications.

What are the practical applications of Agentic AI?

Applications of Agentic AI include:

Tech & Product Teams: Automate workflows, test features, or debug with minimal oversight.
Healthcare: Agents that can monitor patients, analyze medical data, assist with diagnoses, and personalize treatment plans.
Finance: Systems that can manage investments, detect fraud, and provide personalized financial advice autonomously.
Customer Service: Intelligent virtual assistants that can understand user intent, access information, and take actions to resolve issues independently.
Supply Chain Management: Autonomous systems that can analyze demand, predict disruptions, and optimize logistics in real-time.
Robotics: Robots capable of performing complex tasks in unstructured environments, adapting to changes and making decisions on their own.

What makes this course unique compared to general LLM or GenAI courses?

Unlike most courses that focus only on LLM usage or prompting, this one teaches end-to-end agentic system design, multi-agent orchestration, tool integration, and real-world deployment specifically for product management—all guided by FAANG+ instructors.

Do I need prior AI or ML experience to enroll in this course?

No, our Applied Agentic AI for PMs is designed to be accessible to a wide audience, including those with little to no background in coding or machine learning.

What kind of projects will I build in the course?

You’ll build 3 live guided projects and 2 capstone projects of your choice, such as a such as an AI-powered PRD generator, a sales lead optimizer, or your own custom idea—giving you portfolio-ready, real-world AI products.

How much time do I need to commit weekly?

The course runs for 13 weeks, with 30+ hours of live instruction and 15+ hours of live project work. Most PMs commit 10–12 hours per week while managing their full-time jobs.

Who are the instructors?

You’ll learn from current and former FAANG+ professionals and AI leaders who’ve built real Agentic AI systems. Instructors include GenAI leads, ML architects, senior engineers and AI product managers from Google, Amazon, and more.

What tech stack and tools will I learn?

You’ll get hands-on with 20+ cutting-edge tools including LangChain, AutoGen, LangGraph, CrewAI, Pinecone, ChromaDB, Streamlit, OpenAI APIs, Docker, Kubernetes, and more—used in production environments at top tech companies.

What support do I get during the course?

You get access to:

Weekly Technical Coaching session
Practice and project feedback sessions
On-demand TA support over email
Access to recorded sessions for flexible revision

What happens if I miss a live session?

All live sessions are recorded and accessible on-demand. You can catch up anytime and even rewatch for revision.

Is there a payment plan?

Yes! We offer multiple financing options to make the course more accessible to working professionals.