From demo to application
Move beyond a notebook or chat demo into a workflow with context, state, tools, and a clear user outcome.
Course 01 · 5 days
Generative AI Application Developer
Build a production-minded GenAI application from the model layer to retrieval, tools, agents, and a capstone you can demonstrate.
1 · Learner problem
You can call a model, but the application still needs a system around it.
Early GenAI developers often know prompts or APIs but need a practical path through retrieval, tool use, agent boundaries, production quality, and deployment decisions.
Reliable knowledge access
Learn where RAG and enterprise knowledge fit, and how to keep sources and uncertainty visible.
Production judgement
Make room for evaluation, cost, security, observability, and human review before shipping.
One end-to-end build
Use a guided capstone to connect the application layers and explain what you would improve next.
2 · Course outcome
Leave able to explain how a GenAI application works end to end.
LLM application foundations
Choose model interactions, prompts, structured output, and a user-facing workflow.
Knowledge and tools
Use retrieval, enterprise context, tool calling, and controlled actions where they add value.
Production thinking
Review quality, latency, cost, safety, observability, and deployment considerations.
Capstone application
Show a production-ready GenAI application concept with a clear next-iteration plan.
For junior developers moving into GenAI application work.
- Junior GenAI or software developers
- Python or JavaScript developers exploring LLM applications
- Technical graduates with a project they want to build
- Builders who can commit to five intensive days
Bring curiosity and one useful application idea.
Basic programming familiarity helps. The programme is not a passive tool tour; learners build, test, and review throughout the intensive.
4 · Curriculum
Six modules from foundations to capstone.
- LLM Application FoundationsModel interaction, prompting, structured output, and application architecture.
- RAG & Enterprise Knowledge AppsGround responses in trusted knowledge and design retrieval-aware user flows.
- Tool CallingConnect models to bounded tools, actions, and useful application context.
- Agents & MCPUnderstand agent boundaries, tool use, orchestration, and Model Context Protocol.
- Production GenAI EngineeringReview evaluation, safety, cost, observability, testing, and deployment choices.
- Capstone: Production-Ready GenAI ApplicationConnect the modules in a guided application demonstration.
5 · Live project
Build a production-ready GenAI application.
Choose a knowledge assistant, structured content workflow, tool-using helper, or another bounded application brief.
Application architecture
Define the user, model interaction, context, tools, output, and review path.
Quality and safety checks
Document what good output means and how failures return control to a person.
Capstone walkthrough
Show the application flow and explain the next production engineering step.
6 · Trainer credentials
View trainer profileApplied AI taught through working systems.
MHGrad’s practice covers GenAI applications, RAG, agents, MCP, software delivery, operations, and responsible AI workflows. Verified trainer credentials and references are available during enrolment conversations.
Five-day live intensive
- Daily guided instruction and build time
- Live exercises across each application layer
- Capstone review and demonstration
- Exact dates, timezone, and delivery format confirmed before enrolment
8–10 · Fees and enrolment
Current fee shared on consultation.
Ask about the next cohort, availability, payment steps, or a private team delivery. Refund terms are published before payment.
Enquire for current fee
Enrol now11 · Genuine testimonials
Proof is published with permission, not invented.
“
Verified learner feedback is collected cohort by cohort.
MHGrad publishes the learner, role, cohort, project, exact words, and permission status when a story is approved. See the proof policy.
FAQ and refund terms
Before you enrol.
Do I need prior GenAI experience?
Basic programming familiarity is useful, but the course starts with application foundations and builds toward production thinking.
What is the capstone?
A guided production-ready GenAI application demonstration that connects model use, knowledge, tools, agents, and engineering considerations.
Can teams take this course?
Yes. Ask about a private delivery adapted to your team’s stack, workflow, and schedule.
What is the refund policy?
Eligibility, cut-off dates, transfers, and cancellations are stated in the refund policy and enrolment confirmation.
Build your first production-minded GenAI application.
Ask for dates, fee, prerequisites, or a team-training conversation.