AWS Generative AI Developer Professional
capability building,
designed for your organisation.
A custom-built corporate programme for AWS-certified developers, ML engineers, cloud engineers, solution architects, and senior software engineers (3+ years on AWS) building production GenAI applications on AWS Bedrock and the wider AWS AI portfolio. 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.
- AWS AI services portfolio: SageMaker, Bedrock, Q, Comprehend, Textract, Transcribe, Polly, Translate, Rekognition
- Bedrock vs. SageMaker for GenAI — when to use which
- AIF-C01 exam: domains, weights, question patterns
- AWS GenAI specialty (where launched): exam map and preparation
Want the full module-by-module syllabus, sample assignments, and pricing?
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Demonstrable skills your team will apply on live projects.
Architect production GenAI on AWS
Bedrock + Knowledge Bases + Agents with IAM, VPC, observability, cost governance.
Build agents on Bedrock Agents
Multi-step Bedrock Agents with action groups, knowledge bases, guardrails.
Implement enterprise RAG on AWS
Bedrock Knowledge Bases with OpenSearch Serverless, Aurora pgvector, or Pinecone.
Pass AWS AIF-C01 exam
Cohort first-attempt pass rate 87%.
Ship AWS GenAI capstone
Production-grade AWS GenAI application with full observability and cost governance.
Move into AWS GenAI engineer role
Equipped for AWS GenAI engineer roles at AWS partners, IT services, and enterprise cloud teams.
Where your team is now vs where they'll be after the programme.
Where most teams start
- ·AWS-certified at Solutions Architect or Developer Associate level but no Bedrock experience
- ·Limited fluency with AWS GenAI-specific services: Bedrock, Knowledge Bases, Bedrock Agents, Q
- ·Cannot architect production GenAI on AWS with proper IAM, VPC, observability
- ·No AIF-C01 / GenAI specialty exam preparation
- ·Unfamiliar with AWS-specific patterns: PartyRock, Bedrock Studio, Bedrock Guardrails
- ·Limited fluency with the AWS LLM model catalog: Anthropic, AI21, Cohere, Meta, Amazon Titan/Nova
Where they'll arrive
- ✓AWS Bedrock mastery — model deployment, fine-tuning, custom models, Bedrock Agents, Knowledge Bases
- ✓AWS GenAI fluency — Q Developer, Q Business, Q Apps, PartyRock, Bedrock Studio
- ✓Production architecture — VPC, IAM, KMS, CloudWatch, CloudTrail integration for GenAI workloads
- ✓Multi-model selection — Anthropic Claude, AI21, Cohere, Meta, Amazon Titan/Nova on AWS
- ✓Cost & latency optimisation — Bedrock prompt caching, batch inference, provisioned throughput
- ✓AIF-C01 certified — passes AWS Certified AI Practitioner; eligible for AWS Generative AI specialty when launched
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.
Amazon Bedrock provides managed access to foundation models and GenAI APIs, while Amazon SageMaker is a full ML platform for building, training, tuning, and deploying custom machine learning models.