Prompt Engineering Fundamentals
capability building,
designed for your organisation.
A custom-built corporate programme for developers, AI/ML engineers, product managers, content strategists, data analysts, and any professional who works hands-on with LLMs and needs to produce reliable, repeatable, evaluation-grade output. 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.
- Prompt engineering vs. prompt hacking vs. prompt collecting — the three things that get conflated
- Why prompts work: probabilistic generation, in-context learning, instruction tuning, RLHF
- The five qualities of a production-grade prompt: clarity, specificity, structure, evaluatability, robustness
- Why your first prompt is almost never your last — the iteration loop that defines the practice
Want the full module-by-module syllabus, sample assignments, and pricing?
One PDF — sent to your inbox in under a minute.
Demonstrable skills your team will apply on live projects.
Apply 12+ named prompting patterns deliberately
Match patterns to tasks: classification → few-shot, complex reasoning → CoT, factual lookup → RAG-augmented, multi-step → ReAct, ambiguous → self-consistency.
Engineer structured outputs that production code can rely on
Schema-driven generation, tool-calling, function-calling, validators, and graceful failure modes.
Run evaluation-driven prompt development
Reference test sets, automated evaluation harness, CI gates on prompt regressions — the discipline of treating prompts as deployable assets.
Ship production-ready prompts to your team
Capstone deliverable: a versioned, tested prompt library with evaluation metrics that the team can deploy directly.
Reduce LLM operating costs by 30–50%
Prompt compression, model-routing, caching, and prompt-tuning techniques applied to learner's actual production workloads.
Pass GSDC Prompt Engineering Certification
Two attempts included; cohort first-attempt pass rate 91%.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·Writes prompts by intuition — copying from social media, tweaking by feel, no measurable improvement loop
- ·No mental model of why some prompts succeed and others fail across model families
- ·Cannot reliably elicit structured output (JSON, schema-conformant) from models
- ·Unfamiliar with named patterns: Chain-of-Thought, ReAct, Self-Consistency, Tree-of-Thought, Self-Critique
- ·No evaluation discipline — cannot measure whether a new prompt is actually better than the previous one
- ·Treats prompts as throwaway artefacts, not as versioned, tested, deployed assets
Where they'll arrive
- ✓Pattern fluency — applies 12+ named prompting patterns deliberately based on task type
- ✓Structured-output mastery — reliably generates schema-conformant JSON, tool calls, and typed outputs across GPT, Claude, Gemini
- ✓Evaluation discipline — builds reference test sets, measures prompt versions against them, runs CI-gated prompt regressions
- ✓Prompt-as-code — versions, tests, and deploys prompts using promptfoo/PromptLayer/MLflow
- ✓Multi-model fluency — adapts prompts across GPT-5, Claude, Gemini, Llama, with awareness of each family's quirks
- ✓Production prompt portfolio — leaves with 100+ tested, evaluated, documented prompts spanning core enterprise tasks
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.
Prompt engineering focuses on designing clear, structured, and goal-oriented prompts to get accurate outputs from AI models. ChatGPT mastery is broader. It includes prompt engineering, but also covers ChatGPT features, file analysis, custom workflows, productivity use cases, automation, research, content creation, coding support, and business implementation.