Project Planning for AI
If AI is different, and AI projects are different, how do we plan projects for AI? What are the roles and tracks we should consider? What are some common gotchas?
What will we discuss?
- Why AI projects are different from "normal" software projects
- Planning for Context Design and Evals as first-class activities
- How to budget an AI project — and what surprises to plan for
- The roles and skillsets to hire or borrow for AI work
- Common gotchas that derail AI projects
Who is this for?
- PMs, leads, and producers planning their first (or next) AI project
- Anyone responsible for scoping, budgeting, or staffing AI work
- Teams adapting their planning process now that AI is in the mix
Project Planning for AI
- All 3 videos in this series
- Prompts, templates and resources from each episode
- Watch at your own pace, on any device
- Full access to every other course on model context experience
Full access to all courses, $79/m after the trial. Cancel anytime.
What you'll learn
- How to plan for context design and evals from day one
- How to budget an AI project and what to watch out for
- Which roles and skillsets AI projects need
Learn from Peter
Peter Van Dijck
Peter has been building AI products since 2023 and teaching teams how to build them since 2024. He has spent that time in the trenches with evals, observability, synthetic data and context engineering, and turns what actually works into practical, no-fluff lessons.
Every episode is taught by Peter himself, in plain language, for product managers, designers, researchers and strategists who need to understand how AI systems are really built, without needing to be an engineer.
Peter on LinkedInWho this is for
Project and product leads
You are scoping an AI project and want to avoid the common gotchas.
Agency and consultancy leads
You need to estimate and staff AI work for clients.
Team managers
You are deciding who to hire or upskill for AI work.
What's included
- All 3 videos in this series
- Prompts, templates and resources from each episode
- Watch at your own pace, on any device
- Full access to every other course on model context experience
- A planning checklist for evals and context design
What participants say
“This is opening up all sorts of new neural pathways for me to see under the hood more of how the sausage is made! 🙏”
“Very timely at my enterprise software company as evaluation of AI features scales.”
“Everything I know about evals is from Peter's talk, which is why I'm back to find out more!”
The videos
1. Project Planning for AI
Project planning for Evals and Context Design
Context Design and Evals are two cornerstone activities for building great AI products. How to plan for them up front so they don't get squeezed at the end.
Budgets for AI projects
How to budget an AI project realistically: what's predictable, what isn't, and the line items teams routinely forget.
AI project roles
AI projects need a different skill mix. The roles and skillsets to hire, borrow, or grow when you're planning AI work.
Also included
Your subscription unlocks every course on model context experience.
AI 101
Build a deeper understanding of AI. Why do models have a personality? What is context engineering?
Course details →Evals 101
How do we know if our AI systems are working well? *The* key skill for UX researchers and product people.
Course details →Claude Code for non-engineers
Despite the "code" in its name, Claude Code is perhaps the most popular agentic AI system right now. Understanding and using it gives you a glimpse into what's coming the coming months and years in terms of agents. And it can be incredibly useful for non-coding tasks.
Course details →Designing with Claude
A hands-on walkthrough of Claude Design — Anthropic's tool that creates real, code-based designs. Set up a design system, generate and refine a landing page, and see where designing-by-code shines: interactive, animated, production-quality design with a design-to-engineering handoff measured in minutes.
Course details →Evals for agents
How do you build evaluations for agents? Model capabilities are evolving fast, user expectations are shifting, and both inputs and outputs are highly variable. This series walks through how to think about agent evals — from the kinds of agents you might be building, to identifying risk, defining quality, and combining qualitative research with metrics.
Course details →Content Strategy for LLMs
Content strategy is changing now that LLMs are reading, writing, and rewriting most of what we publish. This series is a practical walkthrough for content folks: setting up the right tools, structuring content as markdown, defining tone of voice and microcopy in ways an LLM can actually follow, and evaluating what comes out the other end.
Course details →