Training Programme
A hands-on team context engineering programme — teaching development teams how to build shared context infrastructure for AI, so knowledge compounds across people and sessions instead of resetting to zero. Complements your existing agile process, doesn't replace it.
Your team uses AI every day. But every conversation starts from scratch. No one knows what the AI told someone else yesterday. Decisions get lost between sessions. New team members get no benefit from months of accumulated AI interactions.
Your standups, retros, and sprint planning already generate valuable knowledge — but none of it reaches the AI. The individual tools exist — prompt engineering, context files, memory features. But they don't solve the team problem. Everyone's still working alone, duplicating effort, and producing work that conflicts with decisions already made.
Every AI session starts from zero. Context built in one conversation is gone in the next. Multiply that across a team and the waste is enormous.
Three engineers ask the AI the same architecture question. Three different answers. No one knows the others asked — or what was decided.
AI produces code that contradicts decisions already made. It doesn't know about them. Nobody wrote them down where the AI — or the new hire — could find them.
Six months of AI usage produces no institutional knowledge. No shared memory. No accumulated intelligence. Every day is day one.
The question isn't "how much does AI training cost?"
It's "what's the cost of your team using AI without methodology for another 6 months?"
Context engineering as a team discipline that complements your agile process — not individual prompting tricks. Your team already produces documentation through standups, retros, and decision-making. This programme gives that knowledge a second purpose: the working memory of an AI squad-mate that participates meaningfully in your existing workflow.
Four weeks. Three phases. Real work, not slides.
Teams build shared context infrastructure from scratch on a hands-on exercise project. Every participant builds something real — interdependent services that require coordination, shared standards, and documented decisions. By the end of the week, someone else on the team can pick up your work and continue from context alone.
Teams apply the methodology to their actual codebase, inside their existing sprint cadence. This is where habits form — the training moves from exercise to practice. Context engineering becomes part of your standups, your retros, your pull requests. Twice-weekly office hours provide guidance as the team adapts what they've learned to their own architecture, conventions, and workflows.
Grounded in what actually happened during the sprint — not hypotheticals. Teams learn to maintain, prune, and evolve their context. Introduces the path toward autonomous AI workflows, built on the foundation they've already proven works.
Most AI training stops at "better prompts." This programme builds toward a fundamentally different relationship between your team and AI.
"The AI knows this codebase."
Persistent context that gives AI genuine understanding of your project — architecture, conventions, decisions, and standards. Not a blank slate every session.
"The AI remembers what we decided."
Shared memory across sessions and team members. Decisions compound. Context is maintained, pruned, and evolved — not just accumulated until it's noise.
"The AI has opinions, not just outputs."
AI takes on larger tasks with genuine understanding of team standards. Not blind delegation — informed autonomy built on proven context, verified against explicit criteria.
Built by the team, fully understood — not installed from a template. The team owns it because they created it.
Processes, conventions, and "what good looks like" — written down where both humans and AI can follow them consistently.
Not a chore. Reasoned decisions captured with why, not just what — creating institutional memory that compounds.
New team members — human or AI — orient from shared context without requiring someone to explain everything from scratch.
Explicit standards enable verification against criteria, not blind hope. You trust AI the same way you trust a new hire — through shared, explicit process.
Not a one-off workshop. A progression model the team can continue to develop, with each level building on the last.
Most AI training teaches individuals to prompt better. This teaches teams to work differently — with shared context that compounds across people.
Week 1 uses a structured exercise. The sprint moves to your actual codebase. Week 2 is grounded in what actually happened. No hypotheticals.
Habits don't form in a day. The sprint phase is where the methodology becomes practice — with guidance, not just a certificate.
Context files are table stakes — everyone has them now. The gap is the methodology: how teams share, maintain, and evolve context together.
This isn't a new methodology to adopt — it's a complementary discipline that gives AI the ability to participate in sprints, retros, and standups as a genuine team member. You don't change how you work. You make AI capable of working the way you already do.
Teams of approximately 8 — engineers and product managers working on shared codebases. Not individual contributors; teams that need to coordinate.
Your team already uses Copilot, ChatGPT, Claude, or similar tools. You've seen the value individually — now you need it to work as a team.
You already have sprints, retros, and working agreements. You're past "should we use AI?" and into "how do we make AI part of how we already work?" This programme is the how.