Specialist track

AI / LLM Engineering

The differentiator track for working engineers. Go beyond calling an API: design retrieval systems, evaluate model output, control cost and latency, guard against failure, and ship AI features your users can trust. Part-time, project-driven, taught by practitioners.

Duration: 12 weeks
Commitment: ~12 hrs/week · part-time, evenings
Format: Live online — with optional in-person build sessions in Nairobi
Level: Intermediate · for people who already write code

Specialist track

$1,450

Indicative · your local currency · pre-VAT-determination

Deposit $350, then monthly installments

Cohort 1 · Online

Starts 28 September 2026

Filling fast

Apply now

A short application — no payment on this site.

Refund & deferral policy

What you'll learn

Prompt & context engineering, structured output, and tool/function calling
Retrieval-augmented generation: embeddings, vector stores, chunking that works
Agent loops and graph-based agent orchestration — multi-step workflows with state, checkpointing and recovery
Connecting agents to tools and data with MCP
Evaluation: how to actually measure whether your AI feature is good
Cost, latency, safety and guardrails for real users

AI-native by default

The AI classes in this program

Every Lightspace program teaches the AI-native method — not a bolt-on module, but a working style woven through the projects.

Context engineering as a first-class discipline — the skill that replaced prompt engineering
Agent loops and harness engineering — designing the constraints an agent works inside
Graph-based agent orchestration (LangGraph): state machines, checkpointing, recovery
Evals — systematic measurement of AI output quality, not gut feel

The curriculum

1
LLM & context foundations
Weeks 1–3
How these models actually behave, prompt and context engineering, structured output, tool calling, and building your first reliable pipeline.
2
Retrieval & data
Weeks 4–6
Embeddings, chunking, vector search and RAG patterns that survive contact with real documents and real users.
3
Agents: loops, graphs & tools
Weeks 7–9
The agent loop, harness engineering, and graph-based orchestration with LangGraph — multi-step agent workflows with state, checkpointing and recovery, connected to tools and data via MCP.
4
Evals, reliability & ship
Weeks 10–12
Building evals, measuring quality, cost and latency control, guardrails, and a capstone agentic feature you present and defend.

What you leave with

A deployed agentic LLM feature with retrieval, evals and guardrails
A repeatable evaluation harness you can take to your job
The vocabulary and judgement to lead AI work on your team

How you're taught

Live cohorts led by working practitioners — not pre-recorded videos.

Live, not recorded

Real classes on a schedule, with a group that keeps you moving.

Practitioner instructors

Taught by people who build and ship software for a living.

Feedback on your work

Code review and honest feedback on real projects, every week.

Questions

Where does it take place?

Live online — you can join from anywhere in the world. In-person lab days at our Nairobi base are optional, not required.

How much time does it take?

12 weeks, at roughly ~12 hrs/week · part-time, evenings.

What do I need to start?

Intermediate · for people who already write code.

How do payments work?

There's no payment on this website. You submit a short application; if you're accepted, our team emails you to confirm your place and arrange payment — a deposit, then monthly installments.

What language is it taught in?

All live classes and materials are in English.

Is this for beginners?

No — it's for people who already write code. If you're newer, start with a short course or the AI-Native Software Engineering bootcamp.

Full details in our Refund, withdrawal & deferral policy.