LLMOps & AI Engineering for Production
capability building,
designed for your organisation.
A custom-built corporate programme for ML engineers, MLOps engineers, platform engineers, SREs, DevOps engineers, and senior software engineers (3+ years) responsible for shipping and operating LLM-powered applications in production. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.
A modular syllabus, built to be tailored.
Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements — module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.
- MLOps vs. LLMOps: shared discipline + LLM-specific concerns
- What's LLM-specific: prompt versioning, eval-driven deploy, drift on retrieval/embedding/prompt, cost variability
- What's not LLM-specific: CI/CD, identity, networking, observability fundamentals
- Production maturity model: prototype → pilot → managed-prod → mature-platform
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Demonstrable skills your team will apply on live projects.
Architect production LLM systems end-to-end
Identity, networking, observability, evaluation, cost governance, security — across model, RAG, and agent layers.
Implement eval-driven CI/CD for LLM applications
Reference test sets, automated evaluation, CI gates on prompt/model regressions.
Govern LLM costs at scale
Routing, caching, batching, model selection, prompt compression — with measurable savings against capstone systems.
Pass GSDC LLMOps certification
Two attempts; cohort first-attempt pass rate 87%.
Reduce LLM operating costs by 50–70%
Documented savings on capstone systems via routing, caching, batch, and prompt-engineering.
Lead production AI engineering
Equipped to take an organisation from notebook prototypes to operationally-mature production AI.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Has shipped LLM-powered demos but never operated one in production at scale
- ·Limited fluency with LLM-specific operational concerns: drift, eval-driven CI/CD, cost spikes, jailbreak detection
- ·Cannot architect a production LLM stack with proper observability, evaluation, and cost governance
- ·Familiar with classical MLOps (MLflow, model registry) but unsure how it applies to LLMs
- ·No working knowledge of LLM-specific tooling: Langfuse, LangSmith, Arize, Helicone, promptfoo
- ·Unfamiliar with LLM cost optimisation patterns at scale: routing, caching, batch, prompt compression
Where they'll arrive
- ✓Production LLM architecture — designs and ships full LLM stacks with identity, network, observability, evaluation, cost, security
- ✓LLM-specific tooling fluency — Langfuse, LangSmith, Arize, Helicone, promptfoo for observability and evaluation
- ✓Eval-driven CI/CD — automated regression detection on prompt and model changes
- ✓Cost governance at scale — routing, caching, batching, prompt compression, fine-tune-vs-prompt decisions
- ✓Incident response for LLM systems — playbooks for drift, jailbreaks, cost spikes, hallucination-driven harm
- ✓GSDC LLMOps credential — practitioner-level production engineering certification
Built for L&D outcomes, not seat counts.
Prompt discipline, not prompt luck
Learners move from trial-and-error prompting to named patterns such as role prompting, few-shot, prompt chaining, and self-critique.
Reusable team assets
The programme produces Custom GPTs, reusable workflow templates, and a shared prompt library that teams can govern and scale.
Daily productivity workflows
Labs focus on email, reports, slides, meetings, spreadsheets, research synthesis, and role-based business assignments.
Measured time savings
Capstone workflows document recurring task compression, review-cycle reduction, and before/after productivity improvements.
Responsible enterprise use
Learners practise confidentiality, IP, bias detection, verification checklists, and safe-use protocols before adoption at scale.
Sustainment built in
30-day, 60-day, and 90-day check-ins help learners keep pace as ChatGPT features and frontier models evolve.
A four-milestone path from skill gap to client-ready.
Foundation & baseline
Establish a working mental model of ChatGPT, frontier models, tokens, context windows, hallucination risks, and model-selection trade-offs.
Prompt engineering labs
Learners practise CRISPE, SPEAR, role prompting, constraint-led prompting, few-shot prompting, self-critique, and prompt iteration on real work scenarios.
Custom GPTs & workflow automation
Each learner builds reusable GPTs and connects ChatGPT to productivity tools for email, documents, spreadsheets, meetings, and research workflows.
Capstone & sustainment
Learners demonstrate a personal AI productivity system and continue with prompt-of-the-week, model-of-the-month, and 30/60/90-day check-ins.
Want this curriculum aligned to your tech stack and project archetypes?
Why enterprise teams choose the B2B engagement model.
Domain-expert trainers, not professional presenters.
"My job isn't to teach ChatGPT as a tool — it's to help professionals build repeatable AI workflows, verify the output, and reclaim hours from routine work."
Taught by people who've actually shipped the work.
Built for L&D leaders and their learners.
Who this is for
- ·Knowledge workers who want to apply ChatGPT productively in their daily workflows
- ·Business analysts, consultants, marketing professionals, project managers, and individual contributors
- ·Teams that use ChatGPT for occasional drafting but need reliable, business-grade outputs
- ·Managers looking to establish team-wide prompt standards and safe-use protocols
- ·Organisations that want to automate repetitive work across email, spreadsheets, calendars, and documents
Pre-requisites
- ·No coding prerequisite for business and productivity tracks
- ·Basic familiarity with workplace tools such as email, documents, spreadsheets, slides, and meetings
- ·Willingness to bring real recurring tasks into labs for workflow redesign
- ·Enterprise cohorts should align data-handling expectations before learners use company or client information
Trusted by L&D leaders across the world.
"The programme moved our team from random prompting to a repeatable method. The prompt library and Custom GPTs became assets we could actually reuse."
"The most useful part was workflow automation. Learners took their weekly reports, meeting recaps, and research tasks and reduced hours of repetitive effort."
"Responsible use was handled practically. The team finally understood what can be pasted, what must be masked, and how to verify output before sending it."
Questions L&D teams ask before signing.
MLOps focuses on training, deploying, and monitoring predictive ML models, while LLMOps focuses on prompts, RAG pipelines, evaluation, observability, guardrails, token costs, and production reliability for LLM applications.