InstructorCreated All Levels Mentor Program

AI for Product Teams

Ship AI features confidently — from prototype to production — without a PhD.

0.0 (0 students)
2 months
17 lessons
0 instructors

Last updated 2/27/2026 5:47:40 AM

AI for Product Teams
$997.00 USD

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About This Program

AI is no longer a research project — it is a product feature. This program teaches product-minded engineers how to evaluate AI use cases, build prototypes with LLMs, implement RAG pipelines, set up evaluation frameworks, manage inference costs, and deploy AI features responsibly. No deep ML knowledge required — just solid engineering fundamentals and a product mindset.

What You'll Learn

Evaluate AI use cases systematically to distinguish high-impact opportunities from hype
Build production-ready prototypes using large language models and RAG pipelines
Design prompt engineering strategies that produce reliable
testable outputs
Implement evaluation frameworks that measure AI feature quality beyond simple accuracy
Optimize inference costs through model selection
caching
and request batching strategies
Deploy AI features responsibly with guardrails
monitoring
and human-in-the-loop review

Prerequisites

  • Proficiency in at least one backend programming language (Python
  • JavaScript/TypeScript
  • C#
  • or similar)
  • Experience building and shipping web applications or APIs in a production environment
  • Basic understanding of HTTP APIs and JSON — no machine learning background required

Career Outcomes

Deliver a working RAG prototype integrated with a product use case from your domain
Build an evaluation pipeline that continuously measures AI output quality and drift
Create a cost model that forecasts inference spend at different usage scales
Produce a responsible AI deployment checklist tailored to your product's risk profile
Complete an end-to-end AI feature launch simulation covering build
evaluate
deploy
and monitor

Program Curriculum

4 modules 17 lessons 2 months total
  • When AI Is (and Isn't) the Right Solution
  • AI Use Case Evaluation Framework
  • Building an AI Product Spec
  • Use Case Prioritization Exercise
  • Prompt Engineering for Production
  • RAG Pipeline Architecture
  • Choosing the Right Model
  • Building Your First RAG Prototype
  • Building AI Evaluation Pipelines
  • Human-in-the-Loop Review Systems
  • Handling Hallucinations and Edge Cases
  • Evaluation Framework Lab
  • Inference Cost Optimization
  • Monitoring AI in Production
  • Responsible AI Deployment Checklist
  • AI Feature Launch Simulation
  • Capstone: AI Feature End-to-End

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