The challenge

Most organisations know they need AI capability. They’ve seen the demos, piloted ChatGPT, and have a list of use cases. What they don’t have is a team that can build, deploy, and maintain production LLM systems.

The traditional consulting answer — fly in experts, deliver a system, leave — creates dependency. The team is no better equipped to build the next thing. And the system starts drifting the moment the consultants walk out.

We designed the LLM Residency to solve both problems at once: ship production systems and leave behind a team that knows how to operate and extend them.

The approach

Each residency runs 4-6 weeks as an embedded sprint. We join the client’s team — their standups, their codebase, their tools — and work alongside their engineers to build real systems.

Week 1 focuses on discovery: assessing AI maturity, identifying the highest-value use cases, and setting measurable success criteria. We don’t start building until we know what success looks like.

Weeks 2-3 are the foundation build. RAG pipelines, vector databases, API integrations, and prompt templates — all implemented in the client’s environment, not a reference architecture that needs to be adapted later.

Weeks 3-4 run team enablement in parallel with continued development. Hands-on workshops in prompt engineering, model evaluation, and system debugging. Technical tracks for engineers, applied tracks for product and business stakeholders.

Weeks 4-6 focus on production deployment, monitoring setup, and complete operational handoff. By the final week, the client’s team runs the systems independently while we observe and advise.

Throughout every phase, we document patterns, create runbooks, and implement governance protocols — the operational infrastructure that keeps systems healthy after we leave.

The results

Teams across automotive, real estate, healthcare, and financial services have completed the residency. The outcomes are consistent:

38% improvement in prompt success rates across participants, measured through our LLM Adventure assessment framework
Production RAG and automation pipelines deployed — not prototypes, not slide decks, working systems serving real users
Sustained capability — teams continue building and evolving AI systems independently after the residency concludes

The residency model works because learning by shipping is fundamentally different from learning by studying. When your team deploys a RAG pipeline that answers real customer questions using your actual data, the concepts stick in a way no course can replicate.

The takeaway

AI capability doesn’t transfer through documentation or training sessions alone. It transfers through building together — pair programming, debugging production issues, making architecture decisions with real constraints. The residency embeds that experience directly, so the team that ships version one is the same team that ships version two, three, and beyond.

This engagement was an LLM Residency. Want to see the teaching approach in action? Try LLM Adventure, our free prompt engineering game. Ready to discuss a residency for your team? Book a free call.