ACCELERATOR PROGRAM

AI for Product Teams

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

Joseph Kanon
LED BY Joseph Kanon
0 ENROLLED
2 months DURATION
4 MODULES
CAPITAL COMMITMENT
$997.00 USD
Duration 2 months
Format Self-paced
Level All Levels
SECURE POSITION

About This Accelerator

Program overview and objectives

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.
Self-paced Format
2 months Duration
4 modules, 17 lessons Content
All Levels Level
Included Certificate

Projected Returns

Operational capabilities upon completion

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

Investment Structure

4 modules · 17 lessons · 13h 40m

Develop the judgment to know when AI is the right tool and when it is not. Learn a structured framework for evaluating AI use cases, scoring their feasibility and impact, and writing AI product specifications that engineering teams can actually build.

When AI Is (and Isn't) the Right Solution PREVIEW
AI Use Case Evaluation Framework
Building an AI Product Spec
Use Case Prioritization Exercise

Get hands-on building AI prototypes with large language models. Master prompt engineering for production use, understand RAG pipeline architecture, learn how to choose the right model for your use case, and build a working RAG prototype from scratch.

Prompt Engineering for Production
RAG Pipeline Architecture
Choosing the Right Model
Building Your First RAG Prototype

Learn how to measure and maintain quality in AI features where outputs are non-deterministic. Build evaluation pipelines, design human-in-the-loop review systems, and develop strategies for handling hallucinations and edge cases in production.

Building AI Evaluation Pipelines
Human-in-the-Loop Review Systems
Handling Hallucinations and Edge Cases
Evaluation Framework Lab

Take your AI features from prototype to production. Learn inference cost optimization, production monitoring for AI systems, responsible deployment practices, and bring everything together in an end-to-end AI feature launch simulation.

Inference Cost Optimization
Monitoring AI in Production
Responsible AI Deployment Checklist
AI Feature Launch Simulation
Capstone: AI Feature End-to-End

Deliverables

Tangible assets you'll create

Use Case Prioritization Exercise
Apply the evaluation framework to a set of realistic AI use case proposals. Score each candidate, rank them by expected value, and present your prioritization with clear rationale for what to build first and what to defer or reject.
Building Your First RAG Prototype
Build a complete RAG prototype from scratch in a guided project. Ingest a document corpus, generate embeddings, set up a vector store, implement retrieval logic, wire up a language model for generation, and test the end-to-end pipeline with real queries.
Evaluation Framework Lab
Build a complete evaluation framework for an AI feature. Create a golden dataset, implement automated quality metrics, set up regression tests, design a human review sampling strategy, and configure alerting for quality drift. Submit your framework with documentation.
Capstone: AI Feature End-to-End
Bring together everything from the program in a capstone project. Take an AI use case from evaluation through prototype, build an evaluation framework, create a cost model, prepare a responsible deployment plan, and present your complete AI feature package for peer review.

The Advisory Board

Program architects and mentors

Joseph Kanon

Joseph Kanon

Entry Requirements

Prerequisites for admission

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