AI TRAINING
Engineering production-ready LLM applications
Teach engineers to design, evaluate and operate reliable LLM applications in production.
What it covers
An advanced engineering training covering RAG architectures, agent patterns, evals, observability, cost control, prompt versioning and security (prompt injection, data exfiltration). Participants build a small production-grade RAG service with tests, evals and monitoring. We use Python, LangChain or LlamaIndex as references but principles transfer. Heavy focus on what breaks in production and how to detect it.
What you'll be able to do
- A production-grade RAG reference architecture
- An eval harness for the team's main LLM use case
- Security checklist for LLM features
Topics covered
- RAG architectures and chunking strategies
- Agent patterns and tool use
- Evals: offline, online, regression
- Observability, cost and latency control
- Prompt injection and security
Delivery
Run as 3 days; participants must bring laptops with Docker.
What makes it work
- Tech lead championship and post-training pairing time
- Real internal use case as the lab project
Common mistakes
- Skipping evals and discovering regressions in production
- Ignoring prompt-injection vectors in user-facing apps
When NOT to take this
Don't book this if your team has no production deployment experience — start with backend fundamentals.
Providers to consider
- Altitud Consulting
- Le Wagon
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.