The gap
Generated code is not automatically maintainable software. Production work also needs specifications, architecture, tests, security boundaries, review, and observability.
Intent → Context → Plan → Build → Verify → Learn
Vibe coding can be a useful starting point. When a prototype needs to reach production, people and teams need a way to direct AI with enough context, structure, and judgment to make the software maintainable.
The upgrade
We teach people and teams to move from asking AI for code to directing an engineering process they can inspect, verify, and improve.
Training method stagesExplore a stage
Intent
Name the change
Clarify the problem, the people involved, the constraints, and what a useful change would look like before asking for implementation.
What this looks likeExample: describe the decision a collections workflow must support.
Context
Give the work a frame
Assemble the domain rules, current behavior, relevant files, data boundaries, and decisions that AI needs in order to reason responsibly.
What this looks likeExample: bring the invoice states, current files, and data constraints together.
Plan
Choose the path
Use specifications and architecture to turn a broad request into small decisions that a person and an agent can inspect.
What this looks likeExample: define a small change, its architecture, and the seams to test.
Build
Direct the implementation
Guide AI through bounded work, preserve the system's boundaries, and keep the person responsible for the decisions that shape the result.
What this looks likeExample: ask for one bounded implementation while keeping ownership of the decision.
Verify
Ask for evidence
Use tests, security checks, review, and observability to decide whether the change behaves as intended and is safe to extend.
What this looks likeExample: run tests, review the diff, and inspect the behavior that matters.
Learn
Improve the next loop
Observe the work in context, capture what the team learned, and choose the next adjustment with human judgment.
What this looks likeExample: record what the team observed and choose the next improvement.
Each stage remains readable when JavaScript is not used. Enhanced mode lets you focus on one stage at a time.
AI engineering maturity diagnostic
Before a workshop, guided implementation, or advisory engagement, we map how your team currently uses AI in software work—where speed is creating risk, and what should be strengthened before scaling.
Explanatory diagnostic map · not an assessment
Explore a dimensionDirection and context
Purpose meets usable knowledge
We look at the intent behind AI use, the problems it is meant to solve, and whether the team can give AI the domain context, constraints, and current system knowledge it needs.
What we look forStrategy and intent · context and knowledge
Engineering practice
Speed stays inside a discipline
We examine how the team turns AI-assisted work into maintainable software through decomposition, architecture, testing, review, and clear ownership of decisions.
What we look forEngineering discipline · architecture and testing
Risk and control
Boundaries make scaling safer
We trace how security, governance, data boundaries, and observability shape the use of AI so the team can see what is happening and decide what is safe to extend.
What we look forSecurity and governance · observability
Adoption and capability
The workflow and team can carry it
We look at where AI fits into the team's workflow, how practices are shared, and which capabilities need support before a wider adoption makes sense.
What we look forWorkflow integration · team capability
Each dimension remains readable without JavaScript. Enhanced mode lets you focus on one lens at a time.
What the diagnostic leaves you with
- A current-state map of how AI fits the team's engineering work.
- Prioritized risks and opportunities, grounded in the way the team actually works.
- A practical roadmap for strengthening the next layer of engineering practice.
- A recommended first pilot with a clear decision and review boundary.
A working conversation and evidence-based map—not a score, certification, or external validation.
Start with the way your team works today. We can use the findings to shape a workshop, guided implementation, or advisory engagement.
Discuss a diagnosticIf the operation has friction, we can start there.
Tell us what is happening. A first conversation can help order the problem before we talk about solutions.
Send us a message
hello@capuchito.uk