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A practical curriculum for designing, evaluating, and operating LLM-powered features with clear architecture choices, grounding patterns, and production guardrails.
Objectives focus on applied outcomes teams can take back into delivery work.
Prerequisites keep cohorts productive without assuming research-level ML backgrounds unless noted.
Module topics show the shape of the program; private agendas can reorder emphasis without dropping outcomes.
Module 1
Module 2
Module 3
Module 4
Duration: 2–3 days · Availability: on demand
Tooling is kept portable so private sessions can map onto platforms your team already runs.
It is an engineering-focused workshop. We emphasize application architecture, evaluation, and operational readiness rather than training foundation models from scratch.
No. Exercises use hosted model APIs or provided sandboxes. Local GPU setup is optional and not required for the curriculum.
Yes. Private corporate deliveries can emphasize your languages, cloud provider, and integration constraints while keeping the same learning outcomes.
Inquire about Generative AI and LLM Engineering
Share cohort size, preferred format, and timing. We will confirm availability and recommend a delivery path — including private corporate options.
