Learning that respects constraints: quality, security, and operations.
Our platform is built around the idea that AI is a tool inside a system. We teach how to integrate it into engineering workflows with verification, governance, and measurable outcomes.
The otevaxun method
Every course follows the same loop: Frame → Generate → Verify → Integrate. Learners practice making constraints explicit, using AI for speed, and then proving correctness with tests or evidence.
Frame
You learn to scope tasks with guardrails: performance budgets, dependency policies, security requirements, and non-goals. This prevents "prompt drift" where the assistant changes too much at once.
Generate
AI is used to accelerate drafts: code sketches, refactoring plans, incident notes, data transformations. We teach prompt patterns that produce structured outputs you can diff and review.
Verify
Verification is the heart of our curriculum. Learners build test plans, add assertions, run static checks, and practice sampling strategies when full validation is expensive.
Integrate
The final step is integration into your workflow: pull requests, CI steps, documentation, runbooks, and dashboards. This is where learning becomes organizational capability.
Related reading: From prompts to processes · Hidden debt
What do you want to improve first?
Engineering track: ship faster, keep quality
- Constraint-first prompting for refactors and feature work
- AI-assisted test design: coverage with intent
- PR review rubric to catch silent behavior changes
- Documentation that stays close to code (ADR-style notes)
Data & MLOps track: reliability and observability
- Data contracts and validation checks (schema + semantic)
- Reproducible experiments and model versioning
- Monitoring drift, cost, latency, and quality signals
- Incident response for model regressions
Security track: safer automation
- Threat modeling for prompts, tools, and data access
- Tool schemas and allowlists to reduce attack surface
- Secure-by-default prompt templates for triage and analysis
- Audit logs and human-in-the-loop controls
Leadership track: align people, policy, and metrics
- Define what success means: cycle time, defects, incidents, satisfaction
- Run pilots with evidence, not vibes
- Create a prompt playbook and governance rules
- Train reviewers and establish lightweight assurance
Learning paths
Paths are cohesive sequences that produce portfolio-grade artifacts. Each path includes a capstone project with a review checklist and a post-mortem.
From backlog to production
Plan features with constraints, generate draft implementations, validate with tests, and roll out behind flags. Includes a capstone: refactor a service with measurable performance goals.
Reliable ML systems
Build data pipelines with checks, version experiments, and design dashboards for model and prompt health. Capstone: deploy a small model with monitoring and incident playbooks.
Defense with guardrails
Use AI to accelerate triage while reducing risk. Learn prompt injection basics, tool scoping, and secure logging. Capstone: build a safe triage assistant with audited actions.
Example labs
Labs are designed to feel like real work. You get a scenario, constraints, a repo snapshot (simulated), and a rubric. The AI assistant is treated as a teammate that can be wrong.
Lab: Prompt regression suite
You create a set of "golden tasks" that represent user intents. Then you change a prompt and watch the suite detect subtle quality loss. You add monitoring and a rollback procedure.
Lab: Review-ready pull request
You refactor a function using AI suggestions, but you must produce a clean PR: small commits, tests, and a tight explanation. The rubric rewards clarity and safety.
Lab: Incident notes that reduce time-to-recovery
You practice summarizing an outage with an AI assistant, but you must verify the timeline against logs. The output becomes a runbook update.
Connected analysis: Runbooks for AI systems
Skill signals & analytics
Instead of relying on quizzes alone, we analyze the artifacts learners produce. This helps teams understand what changed after training: not just knowledge, but behavior.
- Engineering: PR clarity, test coverage with intent, rollback notes quality
- Operations: completeness of runbooks, monitoring plans, incident response readiness
- Data: data validation depth, reproducibility, monitoring for drift and cost
If your organization needs additional controls, see Governance & safety.
Governance & safety
AI adoption impacts security and compliance. Our platform content includes practical governance templates: acceptable-use policies for assistants, prompt playbook guidance, and review requirements for AI-assisted changes.
- Data minimization: define what data can be used in training exercises and what must be anonymized.
- Change control: treat prompts and tool configurations as versioned assets.
- Human oversight: define which actions require review (e.g., code merges, policy changes, sensitive queries).
- Auditability: capture high-level traces without storing unnecessary sensitive content.
For privacy commitments and user rights, read our Privacy Policy. If you want a tailored governance workshop, contact us via the form.