Today’s PMs and Engineering Decision Makers are under pressure to "build with AI" — but without the right frameworks, costly mistakes happen fast:

>> Product Managers are expected to launch scalable AI systems and lead roadmaps, yet often lack the frameworks to evaluate models, validate builds, and confidently sign off on launches.

>> Engineering Leaders are tasked with selecting models and designing architectures without fully grasping long-term tradeoffs, leading to slowdowns, spiraling costs, and technical debt.

Without a structured evaluation process, teams lose velocity, waste resources, and build features that fail to scale in production.

🛠️ What you’ll build and apply

In this course, you’ll work live with me to develop a practical, step-by-step AI product decision framework. You’ll apply each concept immediately to real-world use cases and leave ready to guide product and architecture decisions with confidence.

You’ll:

* Walk through real-world product goals and map them to the right model choices — task-specific models, LLMs, or agents.

* Apply a five-point evaluation framework (cost, reasoning depth, latency, UX risks, cloud implications) to guide model selection.

* Make live architecture decisions — when to use APIs, RAG, fine-tuning, or RLHF — and understand the tradeoffs you're committing to at launch.

* Design smarter UX flows based on real model limitations like hallucination risks and context window constraints.

* Pressure-test readiness — by building launch qualification plans, fallback designs, and evaluation checkpoints.

Each exercise ties directly into building your own AI feature decision plan, which you’ll submit for personalized feedback after the course.

🚀 Key Takeaways

This is a breadth-first, system-wide view of AI product delivery, with deep dives into critical decision points that define success or failure.

You’ll:

→ Apply the 5-layer AI stack (UX → App → Model → Cloud → Infra) to real-world product builds.

→ Build your own decision rubric for model and system selection.

→ Evaluate architecture tradeoffs using the AI Product Decision Matrix.

→ Create a reviewed, production-ready AI roadmap balancing business goals and technical constraints.

→ Pressure-test your launch plan using real-world evaluation and risk planning.

After this course, you’ll confidently:

✔️ Pick the right model and integration path based on your product’s needs — not hype.

✔️ Evaluate if your AI feature is production-ready — before wasting resources.

✔️ Lead engineering, UX, and vendor conversations with technical clarity.

✔️ Align executives and teams around a defensible, scalable AI roadmap.

✔️ Avoid costly rework by asking the right technical questions early.

This isn’t a course where you "consume information" — it’s a course where you build real decision frameworks alongside me, apply them live, and walk away ready to lead.

💬 What past learners are saying:

"Nice deep dive from Sriram, love the delivery and how all the topics were articulated!!!" - AI/ML Lead, Amazon

"This is such a great course! It unveiled the magic behind the scenes and grounded the overall view. So valuable for product managers or any role really to have this architect literacy! I appreciate you Sriram for your valuable time & effort!!" — Group Product Manager, CompanyCam

"This course is an excellent choice for anyone looking to get a well-rounded understanding of AI. It demystifies the AI hype, providing a clear and structured overview of the subject. The course covers a wide range of tools and platforms available for AI development. The hands-on projects were a great way to apply the concepts in real-world scenarios. I highly recommend it for professionals, especially engineers and engineering managers, aiming to deepen their AI knowledge and quickly get up to speed with practical applications." - Sr. Engineering Manager, Chamberlain Group