In depthLLM developer training for production, not prototypes
A working demo takes an afternoon; a reliable LLM application takes engineering discipline. This bootcamp teaches developers the patterns our engineers use to build production AI systems for clients, from retrieval pipelines to agents, with a strong focus on evaluation, security and the trade-offs that decide success after launch.
From API calls to structured, reliable outputs
The bootcamp starts with the foundations every LLM feature depends on. Developers work directly with the OpenAI, Anthropic and Gemini APIs, covering streaming, structured JSON outputs, tool calling, retries and token budgeting. We explain how to design prompts as versioned code, how to choose a model for each task based on quality, latency and cost, and how to keep provider dependencies behind a clean interface so that switching models later is a configuration change rather than a rewrite. Every exercise runs in a shared repository that participants keep after the course.
A practical RAG course with vector databases
Retrieval-augmented generation is the most common way to ground a model in company knowledge, and the details decide whether it works. Participants build a complete RAG pipeline over a sample document set: parsing, chunking strategies, embeddings, hybrid keyword and vector search, metadata filtering and re-ranking. We compare pgvector, Milvus and managed vector databases, and show how to keep indexes current as documents change, handle permissions and cite sources in every answer. Participants measure retrieval quality directly, so improvements are based on evidence rather than intuition.
A build AI agents course with guardrails
Agents extend LLMs from answering questions to completing tasks, which raises the stakes. Developers build an agent that plans, calls tools, keeps state and asks for human confirmation before sensitive actions. We cover when an agent is the right design and when a simpler fixed workflow is more dependable, how to limit tool permissions, and how to trace every step so behaviour can be debugged and audited. Frameworks such as LangChain are covered alongside plain code, so developers understand what each abstraction does before relying on it.
Evaluation, deployment and security
Without evaluation, every prompt change is a gamble. Participants create golden test sets, automated scoring, LLM-as-judge checks and regression tests that run in continuous integration. The final day covers deployment: containerised services, observability, rate limiting, caching and cost monitoring. Security runs throughout, including prompt injection, data leakage, output filtering and least-privilege access, so teams leave able to ship LLM features that meet the standards expected of any production system. The patterns apply whether you deploy on your own infrastructure or a major cloud provider.
