Applied Generative AI: Build production-ready LLM apps with RAG, evaluation, and real workflows

Mentor

Learn from practitioners who ship Generative AI systems in real products — from prompt design and RAG pipelines to evaluation, safety, and deployment patterns used in modern engineering teams.

What you'll learn

• How Generative AI fits into real IT and product workflows beyond demos and chatbots.
• Core building blocks of LLM apps: prompting, context, tools, RAG, and evaluation.
• How to design end-to-end use cases: intake, generation, review, and human-in-the-loop steps.
• Practical patterns for reliability, cost control, and quality when shipping GenAI features.
• How to build a portfolio project that shows you can apply Generative AI in a job-ready way.

Course highlights

What makes this course distinctive — from teaching style to signature projects.

Project-first teaching

Every module ends in a working artefact — not slides. You leave with demos you can show in interviews.

Live mentor feedback

Get guidance from practitioners who ship GenAI systems, including reviews of your prompts, RAG design, and evals.

Capstone with industry framing

Build a production-style GenAI workflow with clear success metrics, cost awareness, and a portfolio narrative.

Advantages of this Course

Why this course stands out — the outcomes and support that set it apart.

• Job-ready GenAI portfolio

  Graduate with shipped demos — RAG apps, eval harnesses, and a capstone you can walk through in interviews.

• Mentor from industry practitioners

  Learn patterns used in real product teams, not toy notebooks — with live feedback on your builds.

• Production skills, not just prompts

  Cover cost, latency, evaluation, and reliability so you can ship features employers actually trust.

• Clear beginner → advanced path

  Structured roadmap from foundations to industry-ready delivery — no guessing what to learn next.

• Flexible online learning

  Live sessions with recordings and project deadlines designed for students and working professionals.

• Career-facing outcomes

  Position your work for GenAI product and engineering roles with metrics and a strong portfolio story.

Who this course is for / Eligibility

Check if this track matches your background and goals.

• 1st–4th year engineering and CS students exploring AI careers
• Recent graduates who want applied GenAI portfolio projects
• Working IT professionals upskilling into LLM product roles
• Career switchers with a builder mindset and curiosity about AI

Curriculum for Applied Generative AI — download for the full outline and schedule details.

1. 1
   Beginner2 weeks

   Foundations

   Build intuition for how LLMs behave in products — tokens, prompting, grounding, and basic evaluation.

   • Module 1 — Generative AI foundations
2. 2
   Intermediate2 weeks

   Build LLM applications

   Move from prompts to working apps: architecture, tools, structured outputs, and RAG with citations.

   • Module 2 — Building LLM apps
3. 3
   Intermediate2 weeks

   Production readiness

   Add quality gates, cost and latency controls, observability, and human-in-the-loop workflows.

   • Module 3 — Production readiness
4. 4
   Advanced2 weeks

   Industry-ready capstone

   Ship an end-to-end GenAI demo with metrics, cost awareness, and a portfolio narrative employers recognise.

   • Module 4 — Capstone project

Progression: Beginner → Intermediate → Advanced / industry-ready

Demand in the market

How the skills from this course map to what industry teams are hiring and building for right now.

GenAI is reshaping IT roles

Teams expect engineers who can apply LLMs to real workflows — not just experiment with chatbots.

Employers hire for applied skills

RAG, evaluation, and production patterns are becoming core requirements across product and engineering roles.

Career edge for builders

Professionals who ship reliable GenAI features stand out in interviews and move faster into high-impact projects.

Terms & Conditions

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