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AI & MLAdvanced
AI Native Engineering
An engineering-first course on building software the AI-native way. You learn to work with coding assistants without giving up review and testing, then build product features on large language models: grounding them in your own data, calling tools safely, measuring quality, and controlling cost and latency. You finish by shipping a retrieval-augmented assistant.
- Level
- Advanced
- Weekly effort
- 8 hrs/week
- Format
- Hybrid
- Next batch
- New cohort every six weeks
What you'll be able to do
- Use AI coding assistants to plan, write and review code, and verify what they produce
- Call LLM APIs with structured outputs and sensible error handling
- Build retrieval-augmented generation over your own documents
- Design tool-calling agents with guardrails
- Write evaluation sets and measure answer quality
- Manage prompt, cost and latency trade-offs in production
Syllabus
6 modules, each with hands-on assignments reviewed by a mentor.
- 01
AI-assisted development
- Working with coding assistants and agents
- Specs, context and small reviewable changes
- Testing and reviewing AI-written code
- Security and licensing pitfalls
- 02
How LLMs behave
- Tokens, context windows and sampling
- Prompt structure and structured outputs
- Failure modes and hallucination
- Cost and latency basics
- 03
Retrieval-augmented generation
- Chunking and embeddings
- Vector search with pgvector
- Re-ranking and citations
- Keeping indexes fresh
- 04
Tools and agents
- Function calling
- Multi-step workflows
- Guardrails and input validation
- Human-in-the-loop review
- 05
Evaluation
- Building a test set
- Automated and human grading
- Regression testing prompts
- Tracing and debugging
- 06
Shipping to users
- Streaming responses in a web app
- Caching and rate limits
- Privacy and data handling
- Capstone demo
Capstone project
A document assistant with citations
Build an assistant that answers questions over a document set, cites its sources, and reports its own accuracy on an evaluation set you design.
Tools you'll use
PythonTypeScriptAI coding assistantsLLM APIsEmbeddingspgvectorLangGraphFastAPINext.js
Who it's for
- Developers comfortable with Python or TypeScript
- Final-year students with a programming background
- ML practitioners moving into LLM applications
What's included
- Weekly live workshop plus self-paced lessons
- Mentor review on each milestone
- API credits for course projects
- Lifetime access to course material
- Certificate on capstone completion
Questions About This Course
No, but you do need to be comfortable writing code and working with APIs. The course focuses on engineering software with and on top of LLMs, not training models.
We teach provider-agnostic patterns and use more than one API in exercises so you can switch between them. Course API credits are included.
Short recorded lessons during the week, then one live workshop at the weekend where you build and get feedback.
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Enrol
Join the next AI Native Engineering batch
- A counsellor calls within one working day
- Confirm your batch before paying anything
- New cohort every six weeks, seats capped per batch