Week 1 (Friday): Foundations - the reliable reasoning component
14 lessons · Back to full syllabus
What you keep
How to turn a model call into a dependable, observable piece of software - plus context engineering, token optimisation, and a free-resources bridge to mastery.
You ship
A working `/ask` endpoint that answers reliably - the reasoning core of your capstone.
Watch through (async)
Think Like an AI Engineer (free 10-part series)
Watch this YouTube playlist before or alongside Week 1. Plain-English foundations - how LLMs work, prompting, models, context, agents, workflows, and evals - so the live FastAPI and reliability work clicks faster.
- 1How LLM Actually Works (in Plain English)Watch
- 2How to Prompt AI Like a Pro: The 5-Part FormulaWatch
- 3How to Pick the Right AI Model for the JobWatch
- 4Using AI With Your Files, Images and DataWatch
- 5Context Is Everything: The #1 Skill for Using AIWatch
- 6Vibe Coding for Non-Engineers (and Engineers)Watch
- 7What AI Agents Actually Are (Hype vs Reality)Watch
- 8Build AI Workflows With No Code (n8n, Zapier, Make)Watch
- 9AI Evals: How to Know If Your AI Actually WorksWatch
- 10The Fastest Way to Stay Current in AIWatch
Live session resources
Week 1 live session slides
Foundations deck: model call → reliable component, Playground, FastAPI /ask, structured outputs, guardrails, token economics, model selection.
Open resourceWeek 1 `/ask` demo code (5 stages)
FastAPI starter repo: bare /ask through structured output, guardrails, model routing, and cost readout. Includes Streamlit demo UI and stage smoke tests.
Open resourceLessons
From model call to reliable component
Production means the right shape, at a known cost, with a plan for when it fails - every time.
The OpenAI API and the Playground
Experiment in the Playground first: system vs user messages, temperature, max tokens, and the token counter.
Module 1.1 - Build your first LLM service
Stand up a real POST endpoint with typed models before any theory - the living thing everything else improves.
Module 1.2 - Prompting that ships
Structured outputs, few-shot, and decomposition - three techniques to make output deterministic enough to build on.
Module 1.3 - Output guardrails and validation
Schema, semantic, and safety validation - plus retry patterns - between the model and everything downstream.
Module 1.4 - Token economics and context budgets
Tokens are money and latency. Treat the context window as a budget, not free space.
Module 1.5 - Choosing the right model
Match capability, cost, and latency - and route different steps to different models in one system.
Module 1.6 - Context engineering: the throughline
Prompting, cost, and model choice are all facets of one skill: engineering what the model sees.
Module 1.D1 - Advanced structured outputs and function-calling schemas
Nested schemas, enums, and function calling - when the shape must be airtight and feed code directly.
Module 1.D2 - The provider landscape beyond OpenAI
Anthropic, Gemini, open-weight models - per-task fit and portability across providers.
Module T - Token Optimisation
Nine concrete techniques to cut cost and latency without trading away quality you need.
Module B - Mastering AI Engineering: the free-resources bridge
A curated map from bootcamp graduate to self-directed mastery - almost everything is free.
Week 1 assignment support: Ship Your First AI Endpoint
Copy-paste prompts to deploy your Week 1 POST `/ask` endpoint on Render and prove it with curl + guardrails.
Ship your reliable reasoning endpoint
Deploy a live POST /ask, prove it with curl, post on LinkedIn about how you learned and what you shipped.
Lessons in this module