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Create LLM Systems

A 30-session, hands-on program that takes students from AI system fundamentals to designing, building, evaluating, improving, and deploying AI applications โ€” across foundation models, context engineering, RAG, fine-tuning, agentic systems, and modern AI infrastructure.

A student in a live 1-on-1 AI session with their IIT graduate mentor
LIVE 1-ON-1 SESSION
25 sessions
30
Sessions
IIT graduate mentors
IIT
Graduate mentors
Grades 5 to 12 welcome
5โ€“12
Grades welcome
Capstone research paper
Capstone
Portfolio project
BY THE END OF THE COURSE

What every student can do.

Design AI systems

Understand how AI systems are structured, break problems into models, context, knowledge, tools, and applications, and design system architectures before building.

Build with foundation models

Work with open-weight and proprietary foundation models, compare models based on capability, quality, latency, and cost, integrate LLM APIs, and build multi-model AI applications.

Build AI systems with context and knowledge

Design system instructions, structured outputs, memory, and context pipelines, then connect AI systems to external knowledge through RAG, embeddings, vector search, hybrid retrieval, reranking, and grounding.

Evaluate and improve AI systems

Build evaluation datasets and test suites, measure RAG and LLM performance, use RAGAS and DeepEval, apply LLM-as-a-Judge techniques, and use evaluation results to improve system performance.

Adapt, deploy and showcase AI applications

Adapt models using LoRA and QLoRA, build AI agent workflows with tool use and MCP, apply security and guardrails, deploy AI applications publicly, manage projects with GitHub, and present a portfolio project.

THE MENTORS

CS graduates of IIT, BITS and IIIT.

Fewer than 3 in 100 applicants are selected.

Swarnava

Foundations and CS Specialist

"You can't build AI you don't understand. My job is to make the maths underneath it feel obvious - not intimidating."
  • ๐Ÿ“–Mathematical foundations for ML - probability, statistics, logical reasoning
  • โญ14 years of teaching experience
  • ๐ŸŽ“M.Sc. Mathematics & Statistics, IIT Guwahati
Koutheesh

Programming & Applied AI Specialist

"Students learn AI fastest when they're building something real with it. We start writing code on day one."
  • ๐Ÿ“–Python, Java, data structures & introductory machine learning
  • โญ10+ years of EdTech experience
  • ๐ŸŽ“M.E. Electronics
Prasanth

Programming & AI Engineering Specialist

"Real industry experience changes how you teach. I show students how AI is actually built, not just how it's described."
  • ๐Ÿ“–Python, Java, C++ - algorithms, data processing & ML models
  • โญ7+ years' experience ยท backend & API development
  • ๐ŸŽ“M.S. Computer Science
The curriculum

9 modules, 30 sessions

Module
What you cover
Duration
Sessions
01
AI Engineering Foundations
Understand AI Engineering, distinguish AI models from AI systems, design system architectures, make engineering decisions, and establish a professional GitHub workflow.
2 hrs
1โ€“2
02
Foundation Models
Understand foundation models, compare open-weight and proprietary models, select models based on capability, quality, latency, cost, and reliability, integrate LLM APIs, and build multi-model AI applications.
4 hrs
3โ€“6
03
Context Engineering
Design system instructions and context, work with structured outputs and memory, manage context efficiently, and use programmatic approaches such as DSPy to optimize AI behavior.
4 hrs
7โ€“10
04
RAG Engineering
Build retrieval-augmented generation systems covering knowledge ingestion, chunking, embeddings, vector search, hybrid retrieval, reranking, query engineering, grounding, citations, GraphRAG, and PostgreSQL + pgvector.
7 hrs
11โ€“17
05
AI Evaluation Engineering
Design evaluation datasets and test systems, evaluate RAG and LLM performance, use RAGAS and DeepEval, and apply LLM-as-a-Judge techniques to identify areas for improvement.
3 hrs
18โ€“20
06
Model Fine-Tuning
Understand model adaptation and parameter-efficient fine-tuning, then explore PEFT, LoRA, and QLoRA for adapting open models to focused tasks.
2 hrs
21โ€“22
07
Agentic Systems
Understand agent architecture and tool use, build agent workflows for multi-step tasks, and connect AI applications to external capabilities using MCP.
2 hrs
23โ€“24
08
AI Application Engineering
Apply security and guardrails, improve application reliability and efficiency, work with APIs and deployment workflows, and deploy AI applications for others to use.
3 hrs
25โ€“27
09
Capstone Project
Bring together the skills you've learned to design, build, evaluate, deploy, document, and present an end-to-end AI system as a portfolio project.
3 hrs
28โ€“30
Sessions 28โ€“30

Your child finishes with a complete AI system

In the final three sessions, they design an end-to-end AI system, integrate foundation models and retrieval, evaluate and improve performance, deploy the application, and present it live as a portfolio project.

  • 6 portfolio projects
  • 30 sessions
  • Live deployment
  • Live presentation
What they build
  1. AI System Architect
  2. Multi-Model AI Assistant
  3. Domain Knowledge Assistant
  4. AI Evaluation System
  5. Specialized AI Model
  6. Reliable AI Application
TUITION

The Course Fee.

30 private 1-on-1 sessions with an IIT graduate mentor.

Create LLM Systems
USD
INR
AED
$1,000

for the full 30-session course

$33 per 1-on-1 session ยท 30 sessions total

  • Personalised 1-on-1 mentorshipย across all sessions.
  • Original AI capstone project
    ย published under your child's name.
  • Capstone projectย defended live in the final session.
  • Parent progress updatesย throughout the course.
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