AI TRAINING
GenAI Bootcamp for Engineers: Five-Day Intensive
Engineers leave with a shipped internal AI tool, covering LLMs, RAG, agents, and production evaluation.
What it covers
A five-day hands-on bootcamp designed for software engineers transitioning into AI product development. Participants build and ship an end-to-end internal tool using LLM APIs, retrieval-augmented generation, and agentic workflows. Each day combines short conceptual blocks with extended coding labs, culminating in a live demo of a working product. Evaluation, observability, and responsible deployment practices are woven throughout.
What you'll be able to do
- Integrate multiple LLM providers via API and manage prompts, context windows, and token costs programmatically
- Build a production-grade RAG pipeline with chunking strategies, embedding models, and a vector store such as Qdrant or Weaviate
- Implement an agentic workflow using tool-calling and multi-step reasoning to automate a real internal task
- Evaluate an LLM application using automated scoring frameworks and interpret failure modes
- Deploy and monitor a complete AI-powered internal tool with basic observability and guardrails in place
Topics covered
- LLM API integration (OpenAI, Anthropic, Mistral, open-source models)
- Prompt engineering and structured output design
- Retrieval-Augmented Generation (RAG): chunking, embeddings, vector stores
- Agentic patterns: tool use, function calling, multi-step reasoning
- LLM evaluation frameworks: RAGAS, UpTrain, custom scorers
- Observability and tracing in production (LangSmith, Langfuse)
- Guardrails, safety layers, and cost management
- Shipping an end-to-end internal AI tool: design to deployment
Delivery
Delivered in-person or live-virtual over five consecutive days, approximately seven to nine hours per day including breaks. The format is roughly 25% instructor-led conceptual input and 75% hands-on coding labs. Participants work on a shared project repository from day one and ship their tool by end of day five. Materials include a private GitHub repo, pre-configured cloud dev environments, API keys for major providers, and a reference architecture doc. Remote delivery requires stable internet and a capable laptop; cloud-based Codespaces are provided to eliminate local setup friction.
What makes it work
- Pre-defining the capstone project scope before day one so participants can focus on building rather than deciding
- Assigning a technical coach per four to six participants for real-time debugging support during lab sessions
- Holding a live external demo or stakeholder review at the end of day five to create genuine shipping pressure
- Providing a 30-day post-bootcamp async channel (Slack or Discord) so engineers can ask questions as they extend their tools
Common mistakes
- Treating the bootcamp as theory-first and deferring coding until late in the week, leaving insufficient time to ship a working tool
- Skipping evaluation and observability modules because they feel secondary, then struggling to debug LLM failures in production
- Choosing an overly ambitious capstone project scope that cannot be completed in five days, leading to demoralisation
- Ignoring cost management and rate-limit handling, resulting in surprise API bills and broken prototypes post-bootcamp
When NOT to take this
This bootcamp is not the right fit if the engineering team has zero Python experience or if the organisation cannot provide real internal data and a plausible use case for the capstone — without a concrete problem to solve, the hands-on labs lose their practical grounding and participants leave with toy demos rather than transferable skills.
Providers to consider
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.