FlagshipLLM Foundations
Build, evaluate, and ship a working LLM application end to end.
01
This is the flagship program: a structured walk through the full lifecycle of an LLM-powered application. You start with how models actually process text — tokens, context windows, sampling, and model APIs — and move steadily toward systems you would be comfortable putting in front of real users.
Each module pairs short explanations with hands-on Python work. You will build a retrieval pipeline, add tool calling and guardrails, write an evaluation suite that catches regressions, and package the result as a FastAPI service running in Docker with tracing and cost controls in place.
The program closes with a capstone: you design, build, evaluate, and document an AI application of your choosing. The goal is a project you can explain line by line — including its limitations and the trade-offs you made along the way.
Request path
web client
streams tokens to the UI
api · FastAPI
auth, input limits, rate limits
orchestrator
prompt templates, routing, guardrails
model
hosted or open-weight
retriever
pgvector
tools
allow-listed
Cross-cutting
02
03
How language models turn text into tokens and back, and how to call them reliably from code.
Ground model answers in your own documents with a retrieval pipeline you can inspect and tune.
Let models call functions and coordinate multi-step work without losing control of the system.
Measure whether your application works, and catch it when a change makes things worse.
Package the application as a service and give yourself the visibility to run it responsibly.
Design, build, evaluate, and write up an application that brings the previous modules together.
04
Outcomes describe skills and artifacts you will practice. No certification, employment or career outcome is guaranteed.
05
LLM Application Engineering
Alex focuses on the unglamorous parts of LLM products: input validation, structured outputs and graceful failure. Their sessions are about turning a promising prototype into code a team can maintain.
Sample profile
RAG & Knowledge Systems
Priya teaches retrieval-augmented generation from the data up: parsing, chunking, retrieval and the evaluation that tells you whether any of it is working.
Sample profile
Evaluation & AI Reliability
Jordan teaches how to know whether an LLM feature is good enough to ship — and how to notice when it stops being good enough.
Sample profile
Deployment & LLMOps
Sam covers what happens after the demo works: packaging, deployment, tracing, cost control and the operational habits that keep LLM services predictable.
Sample profile06
No. The program focuses on building applications on top of existing models, not training them. Solid Python and a working understanding of APIs are the important prerequisites.
07
Register your interest and tell us about your experience. We’ll suggest where to start.