Corporate track · 4 sessions · 8 live hours
AI for Engineering & Product
Two things at once: using AI coding tools well, and building AI into your product with the patterns that survive production.
Overview
Ship AI features, and ship faster with AI.
Who it's for: Engineers, product managers, QA and platform teams.
Your team leaves with
- AI coding practices your team agrees on, including review rules
- Working patterns for LLM features in your own stack
- Retrieval, agents and evaluations applied to a real feature
- Security, cost and reliability checks before launch
- Length
- 4 sessions · 8 live hours
- Delivery
- On site, live online or hybrid
- Cohort size
- Up to 25 people
- Tailoring
- Built around your tools and workflows
Curriculum
Session by session: AI for Engineering & Product
4 sessions · 8 live hours
- 012 hrs
AI coding tools in a real codebase
Your team builds: Team conventions for AI-assisted development
- Where assistants help and where they cost time
- Review rules for AI-written code
- Tests, refactors and migrations
- 022 hrs
Building with LLM APIs
Your team builds: A working LLM feature in your own stack
- Prompt and context design in code
- Streaming, retries and timeouts
- Structured output you can trust
- 032 hrs
Retrieval, agents and evaluations
Your team builds: A retrieval-backed feature with an evaluation set
- Retrieval patterns and their failure modes
- When to reach for an agent
- Golden sets and regression testing
- 042 hrs
Security, cost and reliability
Your team builds: A pre-launch checklist for AI features
- Prompt injection and untrusted input
- Token cost, caching and budgets
- Observability, fallbacks and rollbacks
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Tell us your team size, tools and timelines. We'll send a tailored outline and a proposal, usually within two working days.