AI, right on your CV
More and more roles ask for hands-on LLM experience. Build the project that proves you have it.
If your current job hasn't given you the chance to build AI features, you're not alone.
In person, over five Sundays, in Central London, you'll build a working AI assistant from scratch, with the same engineering you'd use in production: retrieval, tools, evals and security.
Learn by building, together in a small group and guided by an experienced tutor.
Face to face, shoulder to shoulder to give you that essential edge in today's difficult job market.
This cohort is closed now
Please register interest so that we can let you know when there is a future cohort available.
Proof, not a certificate.
A working AI project on GitHub, built by you, that you can walk a hiring team through.
Each student builds their own project—not a group project.
By the end of the course, your project shows hands-on experience with:
- LLM APIs, with an eval suite used as regression tests
- Retrieval (RAG) with embeddings in PostgreSQL pgvector
- Tool calling and a custom MCP server
- Context management for long-running conversations
- Security: prompt injection, least-privilege tools, PII
- Cost management and hardening
On demo day you present your work to the group: good practice for explaining your engineering decisions in an interview.
You can already code. You need AI experience.
Developers and contractors in and around London who want AI engineering on their CV, now or before their next move.
Tutorials are everywhere. What's hard is finishing a real project, making the engineering decisions and knowing when the AI feature fails. That's what we do together, in the room, with help when you get stuck.
What you'll need
- Working software development experience
- A laptop with Python and your own AI coding tool
- Time for 5–8h of homework a week
Not for: complete beginners, or anyone after ML theory or model training.
Five Sundays, one product.
Each session adds a capability to your project. Build your own implementation alongside other students, then continue it as homework.
- 10:00–11:00Review homework and Q&A
- 11:00–12:00Introduce the session's topics, with coding examples
- 12:00–12:30Lunch break, food provided
- 12:30–16:00Work on your project
- Five Sundays, 10:00–16:00, in person (1, 8, 15, 22 and 29 November 2026)
- Lean on AI tools for coding so you can focus on the engineering decisions
- Missing one session has an impact, but there are ways to deal with it; missing more than one is not recommended, as it may significantly impact the outcome
This cohort is closed now
Please register interest so that we can let you know when there is a future cohort available.
Learn from someone who's made the move.
A software developer with 25 years of experience who has already transitioned into AI engineering.
Foundations
Start where engineers are strongest: define the product, make a working baseline and test its behaviour before adding complexity.
Project
- Define your project, its expectations and what is essential
- Set up the tools you'll use throughout the course
- Protect against untrusted user input
LLM API
- Understand the available LLM APIs
- Make calls and build up messages from a conversation
Testing
- Use an eval suite as regression tests for the AI feature
- Optimise prompts using automated tests
Retrieval (RAG)
Move beyond a model's built-in knowledge: add domain documents and test whether the right information reaches the answer.
Core
- Add a document with domain knowledge using embeddings
- Use a text embeddings model to create the embeddings
- Store and query embeddings in PostgreSQL pgvector
- Prompt the LLM to give references for its answers
Testing
- Retrieval regression tests (query → expected chunk)
- End-to-end golden set for the feature
Security
- Indirect prompt injection via retrieved documents
- PII in the document corpus
Extras
- Chunking strategies, hybrid search, reranking and failure modes
- When not to use RAG
This session is deliberately light, leaving time to catch up if week 1 was a difficult start.
MCPs & Tools
Give the AI feature access to product capabilities while retaining the engineering controls you'd expect around any integration.
Core
- Understand tool calling
- Build ReAct loops with tool calling
- Create a custom MCP for your tools
Testing
- Assert tool calls using mocked tools
- Test when the loop terminates
Security
- Least-privilege tools and argument validation
- Confirmations for dangerous actions
- MCP trust boundaries
Extras
- Planning patterns and guardrails
- When agents are overkill
Context management
Treat context as a limited resource: decide what to retain, what to compact and how to spot failures when information is lost.
Core — compaction
- Context as a capped budget, triggers and keep-zones
- Summarisation vs. pruning and masking
- Context drift and dropped-constraint failure modes
Testing
- Constraint-preservation tests through compaction
- Run the full regression suite
Security
- Compaction must not drop safety constraints
Extras
- Rewind/undo: checkpointing, conversation rollback, regeneration, agent-state recovery
- Long-term memory: user-scoped cross-session memory; minimal in-house build, with Mem0 as an off-the-shelf shortcut
- Memory testing and security: recall tests, PII in memory, cross-user scoping, memory poisoning
Advanced topics + demo day
Step back from the implementation: consider cost, how specialised agents divide work, and what it takes to harden an AI feature. Then show what you built.
10:00–12:00 · Presentation
- Cost management: token accounting, caching economics, model routing, cost caps
- Distributed/specialised agents: their own context and specialty, context isolation, when to split
- Hardening checklist: rate limits, abuse, PII in logs/traces, key review
12:00–16:00 · Demo day
- Brownbag demos
- Completion feedback
Engineering by doing.
- Learn by doing, not by powerpoint
- Everyone moves at their own pace
- Students can choose what they want to focus on
This cohort is closed now
Please register interest so that we can let you know when there is a future cohort available.