Generative AI Training
Build real products on top of LLMs.
Move beyond prompting ChatGPT and learn to engineer applications on top of large language models — prompt design, retrieval pipelines, fine-tuning, and evaluation, all grounded in shipped projects.
Learn to engineer with generative models, not just prompt them
Anyone can type a prompt into a chat window. Building a reliable product on top of a language model is a different skill entirely — it means understanding context windows, embeddings, retrieval, and how to keep a model grounded in your own data.
This program teaches the full applied stack: prompt engineering patterns, working with LLM APIs, building retrieval-augmented generation (RAG) pipelines, and fine-tuning smaller models for specific tasks — all through projects you build and deploy yourself.
What you will learn
Practical skills for building products around large language models, not just using them.
Prompt Engineering Patterns
Design reliable prompts using few-shot examples, chain-of-thought, and structured outputs.
Working with LLM APIs
Integrate large language model APIs into real applications with proper error handling.
Embeddings & Vector Search
Convert text into embeddings and search them for semantic relevance.
Retrieval-Augmented Generation
Build RAG pipelines that ground model responses in your own documents and data.
Fine-tuning Fundamentals
Understand when and how to fine-tune or use parameter-efficient methods like LoRA.
Evaluation & Guardrails
Measure output quality and reduce hallucination with evaluation and guardrail techniques.
Technologies & tools covered
Training roadmap
From prompting fundamentals to a deployed, retrieval-grounded AI application.
01. LLM & Prompt Engineering Foundations
2 Weeks- How LLMs work
- Prompt patterns
- Few-shot examples
- Structured outputs
02. Building with LLM APIs
2 Weeks- API integration
- Streaming responses
- Rate limits & cost control
- Error handling
03. Embeddings & Retrieval
2 Weeks- Text embeddings
- Vector databases
- Semantic search
- Chunking strategies
04. Retrieval-Augmented Generation
2 Weeks- RAG architecture
- Document pipelines
- Context ranking
- Citations
05. Fine-tuning & Customization
1 Week- Fine-tuning basics
- LoRA & PEFT
- Dataset preparation
06. Evaluation & Capstone Project
1 Week- Output evaluation
- Guardrails
- Deployment
- Final project demo
Real-world projects you'll build
Applied builds that mirror how companies actually use generative AI today.
Document Q&A Assistant
A RAG-powered assistant that answers questions grounded in a private set of documents.
AI Content Co-pilot
A writing assistant that generates and refines marketing copy from short briefs.
Support Ticket Summarizer
A tool that summarizes and tags incoming support tickets to speed up triage.
Internship & industrial exposure
Applied AI Problem Briefs
Work on problem statements modeled after real generative AI product requests.
Mentor Reviews on Prompts & Pipelines
Get feedback on prompt design and RAG architecture from practicing AI engineers.
Iterative, Evaluation-driven Workflow
Practice the test-and-refine loop real AI teams use to improve output quality.
Learning methodology
Build-and-evaluate Cycles
Every module ends with shipping something and measuring how well it performs.
1:1 Mentorship
Weekly sessions with a mentor experienced in applied LLM development.
Applied Assessments
Evaluated on working pipelines and outputs, not multiple-choice quizzes.
Eligibility
Basic Python Knowledge
Comfort reading and writing basic Python is expected; we build API and pipeline skills from there.
Any Educational Background
Open to students, developers, and career switchers curious about applied AI.
No Prior ML Experience Required
We don't require deep machine learning theory — the focus is applied engineering.
Consistent Time Commitment
8–10 hours a week for hands-on practice and project work.
Career opportunities
Generative AI Engineer
Build products and pipelines powered by large language models.
AI Application Developer
Integrate LLM capabilities into existing products and workflows.
Prompt Engineer
Design and optimize prompts for reliability, cost, and accuracy.
ML/AI Product Engineer
Bridge product requirements with practical generative AI implementation.
AI Solutions Consultant
Advise businesses on where and how to apply generative AI effectively.
Freelance AI Developer
Deliver LLM-powered features and tools for clients.
Why choose YashOrbit
Applied, Not Theoretical
Focused on shipping working AI features, not abstract ML theory.
Placement Assistance
Resume reviews, mock interviews, and referrals to hiring partners.
Real Deployment Experience
Every project is deployed and demoed, not left on a notebook.
Frequently asked questions
No. This program is applied-engineering focused — you'll learn to build with existing models via APIs and fine-tuning, without needing deep ML theory first.
Ready to build products on top of generative AI?
Join the next batch and go from prompting to shipping deployed, retrieval-grounded AI applications.