End-to-end AI for IT

Three-day intensive program that takes your IT team through the whole development cycle with AI — from requirements analysis through implementation and tests to deployment and governance.

• Length: 3 days (3 × 8 hours)
• Price: 36,000 CZK / participant (ex-VAT)
• Format: on-site, custom-built for your team, on demand
• Max participants: 15
• Who it's for: IT teams that want to embed AI in the whole working process — from planning to production
• Prerequisites: basic IT experience. Prior AI experience isn't required but speeds things up.

• Use AI in every phase of the development cycle — analysis, planning, implementation, testing, review, debugging, documentation
• Work with AI systematically, not randomly — context, rules, deterministic checks
• Critically evaluate AI outputs — know where AI hallucinates, where it hides problems, and where it actually adds value
• Set up AI infrastructure for the whole team — shared configuration, context files, governance rules
• Measure AI's impact on your work — not "AI increased productivity by X %", but concrete metrics relevant to your team

• How AI tools actually work — tokens, context, limits. No hype, no marketing.
• The AI assistance gradient: from inline suggestions to autonomous agents. Everyone finds their point on the scale.
• Practical work with AI assistants — working modes, adding context, team rules
• Pair work on real-world exercises

• Working with raw materials — emails, meeting notes, conflicting requirements
• AI as an analysis assistant: extracting requirements, identifying conflicts, BRD
• Epic → user stories → acceptance criteria → diagrams → API spec
• Decision points at every step: AI provides a draft, humans decide
• DoD validation — verifying output quality against a template

• Tests as context — why write the spec before the code
• Shift-left: spec/test → AI implements → deterministic check (test pass/fail)
• Iterative implementation: plan → small tasks → each verifiable
• AI-powered code review: what it catches and what it doesn't
• Debugging with AI: root cause analysis, working with logs and error messages
• Live demo on use-cases chosen by participants

• Code review with AI as a second reviewer, not a replacement for engineering judgment
• Debugging from failing tests, logs, and production-like error reports
• Turning implementation output into documentation and release notes
• Team workflow design: where AI helps, where deterministic checks decide

• "Sketch your SDLC" — each team maps its process and finds the places for AI
• Context files (AGENTS.md) — how one file improves the whole team's outputs
• Shared team configuration — commands, skills, rules versioned in the repo
• MCP — connecting AI to your internal systems (intro + demo)

• Governance in practice: what may go into AI, which tools for what, a one-page AI playbook
• How to measure impact and how to report it
• Concrete next steps: what to do tomorrow, this week, this month
• Wrap-up, Q&A, discussion

• Cohesively. Three days cover the entire development cycle — nothing stays theoretical.
• Practically. Exercises take ~80% of the time. Participants work with materials that simulate real projects — conflicting requirements, missing information, hidden dependencies.
• Decision points. Every exercise has a place where AI output isn't enough and a human has to think. No "copy the prompt, move on."
• Live demo. We show AI on use-cases participants choose from their own SDLC — not a pre-cooked demo.
• In small groups. Max 15 participants, 2 trainers, work in pairs and small groups.

• IT teams of 5–15 people who want to adopt AI systematically, not individually
• Mixed-seniority teams — the program works for juniors and seniors thanks to pair work and the gradient approach
• Companies where someone already uses AI but unsystematically — we normalize AI usage and give it rules
• Companies where AI skepticism exists — we don't show hype but realistic benefits and limits

• Not three identical days. Each day has a different focus: Day 1 analysis, Day 2 implementation, Day 3 infrastructure and governance.
• Not whole-company AI transformation in three days. It's a foundation a team can build on. We also offer longer-term collaboration.
• Not a tool-specific course. The principles work across different AI assistants and development environments.

1. Intro call — we go through your environment, roles, processes, and training goals.
2. Material preparation — we adapt exercises, materials, and the demo to your context.
3. Training (3 days) — ideally on-site, in an environment where your team feels comfortable.
4. Follow-up — summary, recommendations, help with first steps. If you want to go further, we propose a continuation.