What do I need to know before starting?

The entry expectations are listed on this page. If you are close but not quite there, tell us at application; we run short pre-course preparation material and would rather get you ready than turn you away.

Does this course include the internship?

Yes, for learners who complete the core modules and meet the mid-point assessment bar. The internship is on live client work with a supervisor and code review, and you receive a written reference describing what you actually did.

Is there a certificate?

Yes, on completion of the capstone. More usefully, you finish with deployed projects, a public repository history, and a supervisor’s reference. Those are what interviews actually turn on.

What roles does Generative AI prepare me for?

Typical destinations are Generative AI Engineer, LLM Application Developer and AI Engineer. The curriculum is built backwards from what those interviews and first ninety days actually require.

Academy track78 hours · Intermediate

Generative AI

Name: Generative AI
Availability: InStock

LLMs, LangChain, LangGraph, RAG, agents, observability and evaluation

Apply for this trackPreview the syllabus

Fee on request

Duration: 78 guided hours
Level: Intermediate

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Next cohort intake is open · instalments available

Why this track

Build applications on top of large language models that hold up beyond the demo. You design robust prompts and structured outputs, build grounded RAG over real documents, wire up stateful LangGraph workflows and tool-using agents, then trace, evaluate and deploy what you built.

78hours of guided, hands-on learning

10modules, each ending in something deployed

3portfolio projects, reviewed like client work

3+roles this track prepares you for

Outcomes

What you will be able to do

Build model-powered applications with robust prompts and structured outputs
Implement document ingestion, vector retrieval and grounded RAG answers
Build stateful workflows and controlled tool-using agents
Instrument traces, evaluate quality and deploy an interactive GenAI application

Before you start

Entry expectations

If you are close but not quite there, tell us at application. We would rather get you ready than turn you away.

01Comfortable Python, APIs and JSON
02Git basics
03Introductory NLP or embeddings is helpful, not required

Tools you will use

PythonLangChainLangGraphFAISS / ChromaLangSmithRagasFastAPIStreamlitGit & GitHub

Syllabus

A first look at the route

10 modules over 78 guided hours. Here is how it starts; the full roadmap opens up when you join.

M01LLM and Generative AI foundations
- Generative AI use cases, model families and limitations
- Tokens, context windows, embeddings and transformer recap
- Pretraining, instruction tuning, fine-tuning and LoRA overview
- Model selection: capability, latency, privacy and cost
M02Prompt engineering and model APIs
- Prompt structure, roles, context, examples and constraints
- Structured outputs, JSON/schema validation and retries
- Model API integration, streaming and error handling
- Prompt testing and versioning

8 more modules in the full roadmap

The complete syllabus, with every topic, hands-on lab and the capstone brief, is shared with learners when they join the course.

Join the course

Portfolio

What you will have built

01A document-grounded assistant using RAG, with source citations, LangGraph workflow and state, trace logging and a small evaluation dataset
02A Chat-with-PDF RAG app deployed via Streamlit
03A multi-step, tool-using LangGraph workflow with human approval

Where it leads

Roles this prepares you for

Generative AI EngineerLLM Application DeveloperAI Engineer

Questions

Before you apply

The course is 78 guided hours of explanation, guided coding and review, plus independent practice; admissions will share the cohort schedule. Live sessions are scheduled outside standard office hours and recorded, so working professionals can keep a full-time job while studying.