BUILDING LLM APPLICATIONS
LLM application development and evaluation
We help engineers with programming experience build an LLM application, covering retrieval, evaluation, safeguards and deployment.
- The syllabus is the production stack, taught layer by layer
- Every module is a lab: ship a slice, break it, instrument it
- Learn while building a real application from your own roadmap
We teach the stack bottom-up, the order in which it actually breaks.
Each layer is a module, each module is hands-on.
By the end, engineers have built each layer and understand how a weakness in one surfaces as a failure in another: poor chunking looks like a hallucination; a missing eval gate looks like a mystery regression weeks later. This is the engineering track of the full [AI Upskilling and Training](/ai-upskilling-and-training) path.
Most teams can wire a model to a chat box.Far fewer can keep one in production.
Engineers build on the tools they will use in production, on your stack where possible.
Each module is a lab: engineers ship a working slice, break it deliberately, and instrument it.
Where it fits, the program runs learn-while-building: the cohort develops a real application from your roadmap, so the output is production code your team owns. It pairs naturally with [Scaled GenAI and AI Platforms](/scaled-genai-and-ai-platforms), so trained engineers build on a real platform.
Four steps, from level-set to a real application your team runs.
Level-set
Assess the cohort's current skills and pick a build target from your roadmap.
Build the stack
Work through the modules layer by layer, each as a hands-on lab.
Harden
Add evaluation, guardrails, and cost controls, the difference between a demo and a system.
Ship and instrument
Deploy a real application, with observability and a feedback loop your team keeps running.
Experienced engineers ready for fine-tuning, advanced retrieval, and agent design continue into Advanced LLMs. Business teams applying these systems, rather than building them, take AI and GenAI Training.
Assess engineers through a working application.
A tested build.
Complete an application exercise with retrieval or model integration appropriate to the problem.
An evaluation harness.
Demonstrate tests for expected answers and failure cases before accepting a change.
Operational trade-offs.
Explain latency, inference cost, access controls and monitoring choices in the final review.
The engineering track on a role-based path.
Advanced LLMs
Fine-tuning, advanced retrieval, agent design, and real evaluation depth for experienced teams.
AI and GenAI Training
For teams applying these systems rather than building them.
AI Literacy Program
A shared, practical grasp of what AI can and cannot do, and how to use it responsibly.
AI for Beginners
Confidence with core generative AI concepts through hands-on, guided exercises.
Scaled GenAI and AI Platforms
The real platform trained engineers build on, so learning turns into shipped systems.
AI Upskilling and Training
The full role-based path this program belongs to, from literacy to production engineering.
What engineering leads ask us first.
Working software-engineering skills and comfort with an API and a codebase. No prior ML experience is required; we start at LLM application patterns, not model math.
Yes. We adapt labs to your vector store, orchestration framework, cloud, and models so the skills transfer directly to your production environment.
Yes, learn-while-building is our preferred mode. The cohort develops an application from your roadmap, so the output is code your team owns and runs.
Design a hands-on cohort mapped to your stack and a real build target.
The syllabus is the production stack, taught layer by layer, so engineers ship a system your team owns and keeps running.