Starts March 9, 2026

Master the Art of Context Engineering for LLMs

An 8-day hands-on workshop where you'll learn to architect, optimize, and ship production-grade AI agents. From RAG pipelines to tool orchestration to memory systems.

Mar 9 – 20, Mon–Thu9:30 – 10:30 AM ISTLive & Virtual

Build with

Claude
OpenAI
Google Gemini

Taught by MIT & IIT Alumnus

MIT
IIT Madras
8
Days
20+
Exercises
3
AI Models
1
Production Agent
The Problem

Why Context Engineering?

Prompt engineering was the beginning. Context engineering is what actually works in production.

Prompt Engineering Isn't Enough

Single prompts can't solve complex tasks. Modern AI applications need carefully orchestrated context: retrieval, tools, memory, and instructions working together.

Context Is the Bottleneck

LLMs are only as good as what you feed them. Poor context leads to hallucinations, missed information, and unreliable outputs, no matter which model you use.

The Industry Has Moved On

Top AI teams now hire for context engineering, not prompt engineering. Learn the systematic discipline that separates production AI from toy demos.

Core Concepts

Visualizing Context Engineering

Understanding the building blocks of production AI systems:from context architecture to token optimization.

The Three-Layer Context Model

Every LLM call assembles context from three distinct layers:Instructional, Knowledge, and Tool:each with its own engineering discipline.

Three-Layer Context Model for LLM Context Engineering

RAG Pipeline Architecture

The complete retrieval-augmented generation flow:from raw documents through chunking, embedding, and vector storage to grounded AI responses.

Retrieval-Augmented Generation Pipeline Architecture

Agent Architecture

The capstone agent integrates all six modules:instructional context, RAG, MCP tools, memory, guardrails, and observability:into a single orchestrator.

Production Context-Engineered AI Agent Architecture

Token Budget Allocation

How production applications allocate their context window across different types of context.

context_window: 128K tokens
15%
35%
15%
15%
System Instructions
Few-Shot Examples
RAG Context
Tool Results
Conversation History
Available for Response
Who Is This For

Built for Builders

Whether you're writing code or leading teams, this workshop gives you the skills to build reliable AI systems.

Software Developers

You build apps and want to integrate LLMs effectively, beyond simple API calls. Learn to architect context-aware systems.

PythonAPIsFull-Stack

Data Scientists

You work with data and models but need to bridge the gap between experiments and production AI applications.

ML PipelinesEmbeddingsRAG

ML Engineers

You deploy models and want to master the context layer: RAG, tool orchestration, memory, and token optimization.

MLOpsLLMOpsAgents

Tech Managers

You lead teams building AI products and need to understand what context engineering is and why it matters.

StrategyArchitectureLeadership
8-Day Curriculum

What You'll Learn

Two weeks, four sessions each. Monday–Thursday, 9:30–10:30 AM IST. Progressive complexity from foundations to production.

Week 1Mar 9 – 12
Day 1

Context Foundations

  • The paradigm shift: prompts → context engineering
  • Three-layer context model deep dive
  • Context windows & token economics across models
  • Multi-model API calls (Claude, GPT, Gemini)
EXERCISE: Build a multi-model context analyzer
Day 2

The Instructional Layer

  • System prompts as operating systems for LLMs
  • Instruction hierarchy & priority management
  • Few-shot example curation & selection
  • Dynamic prompt assembly patterns
EXERCISE: Design an adaptive instruction engine
Day 3

RAG & the Knowledge Layer

  • RAG pipeline architecture end-to-end
  • Chunking strategies & embedding models
  • ChromaDB vector store setup & querying
  • Hybrid search: semantic + keyword fusion
EXERCISE: Build a complete RAG pipeline with ChromaDB
Day 4

Advanced RAG & Evaluation

  • Re-ranking & contextual compression
  • Multi-index & hierarchical retrieval
  • RAG evaluation: faithfulness, relevance, coverage
  • RAG vs. fine-tuning decision framework
EXERCISE: Build a RAG evaluation harness with scoring
Week 2Mar 16 – 19
Day 5

Tools, MCP & Function Calling

  • Function calling across Claude, GPT, Gemini
  • Model Context Protocol (MCP) servers
  • Tool orchestration & chaining patterns
  • Error handling & fallback strategies
EXERCISE: Build an MCP server with tool orchestration
Day 6

Memory & Context Management

  • Memory architectures: Write / Select / Compress / Isolate
  • Conversation history management strategies
  • Sliding window & summarization patterns
  • Multi-turn reasoning & context carryover
EXERCISE: Implement a memory-augmented conversation system
Day 7

Token Optimization & Observability

  • Token budget planning for production workloads
  • Context compression techniques
  • LLM observability: tracing, logging, metrics
  • Cost optimization & caching strategies
EXERCISE: Build an observability dashboard for LLM calls
Day 8

Production Capstone

  • Capstone: Full-stack AI agent (RAG + Tools + Memory)
  • Production deployment patterns & guardrails
  • Strategy playbook creation for your team
  • Architecture review & next steps
EXERCISE: Ship a production-grade context-engineered agent
Deliverables

What You'll Build

Walk away with two tangible outputs: code you can deploy and a playbook you can reference.

Instructions
RAG
MCP Tools
Memory
Guardrails
Observability
AgentContext Engine

Production AI Agent

A fully functional context-engineered agent that combines RAG retrieval, tool orchestration via MCP, and memory management, ready for real-world deployment.

  • Multi-model support (Claude, GPT, Gemini)
  • RAG pipeline with ChromaDB
  • MCP tool server integration
  • Memory & context management
  • Token budget optimization
  • Error handling & observability

Strategy Playbook

A personalized context engineering playbook with decision frameworks, architecture patterns, and optimization strategies for your specific use case.

  • Context architecture decision tree
  • RAG vs. fine-tuning guidelines
  • Token optimization checklist
  • Tool selection framework
  • Memory pattern catalog
  • Production deployment checklist
Your Instructor

Learn from an MIT & IIT Alumnus

Taught by someone who builds AI systems and publishes research, not just talks about them.

Dr. Sreedath Panat
MIT PhD

Dr. Sreedath Panat

Co-founder, Vizuara AI Labs

PhD from MIT, B.Tech from IIT Madras. 10+ years of research experience. Dr. Panat brings deep technical expertise from both academia and industry to make complex AI concepts accessible and practical.

MIT
IIT Madras
LLMsSLMsVLMsComputer VisionVision TransformersAI AgentsFinetuningRAGContext Engineering
Past Workshops

Trusted by Hundreds of Students

Across multiple workshops, our alumni have built real skills and shipped real projects.

AI Agents 10-Day Workshop

Modern Robotics Workshop

Finetuning + RAG + Memory

Vision Transformers Workshop

Testimonials

What Our Alumni Say

Hear from professionals enrolled in our previous courses.

I had been the student of Raj for two courses, the Generative AI Fundamentals and also the Building LLM from Scratch. Personally, this journey was absolutely enlightening for me because of very unique pedagogy that Raj follows in his teaching style. First is his approach is absolutely no nonsense. He goes to the details of a working code and explains everybody on how actually the entire concept is working on grassroots. But at the same time, he has this beautiful ability to actually abstract things whenever required because these concepts are so complex and so deep that it's very easy to lose the track. A great hands-on experience, lot of practical sessions, and above all, a lot of focus on understanding and building things from scratch.

Samrat Kar

Software Engineering Manager, Boeing

I recently participated in Vizuara's live courses, "Gen AI Fundamentals" and "Building LLM from Scratch," and was thoroughly impressed. Unlike self-paced YouTube learning, the live interactive sessions provided immediate clarification of doubts, vibrant discussions with peers, and engaging interactions with industry professionals deeply interested in generative AI and large language models. The course content, delivered by Dr. Raj, was exceptionally detailed, covering historical contexts, technical intricacies, and practical coding exercises. Dr. Raj's passion, dedication, and approachable teaching style ensured all participants could comfortably follow along, irrespective of their prior experience. I highly recommend this course to anyone eager to advance their knowledge in generative AI and LLMs.

Aman Kesarwani

Quantitative Researcher, Caxton Associates, NY. Ex JP-Morgan

I have recently completed GPT from scratch course with Vizuara. I really love the course. Especially the interaction between the instructor, Dr. Raj and the students. Explored the concepts in depth. Also the assignments. They helped me experiment and iterate and really understand how GPT works under the hood. If you are really serious about learning tokenization, attention mechanisms and transformers, this is the course for you. I highly recommend it.

Kiran Bandhakavi

Product Manager, Navy Federal Credit Union

This bootcamp was one of the most complete and intuition-building journeys I've taken in modern vision: starting from the CNN to transformer transition, then going deep into attention/embeddings, and all the way to ViTs/Swin, detection & segmentation (DETR/Mask2Former/SAM), VLMs (CLIP/BLIP/Flamingo), and multimodal LLMs. Huge credit to Sreedath Panat - rare combo of research-grade depth + crystal-clear teaching. The long-form, "code-along like a real class" style made the ideas stick.

Sri Aditya Deevi

Robotics + Computer Vision Researcher, Aerospace/Field Robotics

Transformers for Vision was by far the longest and most rewarding bootcamp I have ever taken. One of the biggest highlights for me was going from just reading about transformers to actually implementing them from scratch. That hands-on process gave me insights I could never get from theory alone. The course didn't just show what works, it explained why it works. It was both challenging and genuinely fun. By the end, I felt confident navigating complex papers and building my own transformer-based models, which once felt far out of reach.

Koti Reddy

Software Developer

I thoroughly enjoyed this course and found it extremely valuable. I've always appreciated Sreedath's teaching style, he has a great ability to break down complex concepts into simple, easy to understand explanations. The hands on coding sessions that followed the theory were particularly helpful in strengthening my understanding and applying the concepts practically. I chose this course because while many professionals are focusing on RAG and GenAI concepts, I wanted to differentiate myself by learning something more specialized. Diving into Vision Transformers turned out to be a great decision, and this course exceeded my expectations. I would highly recommend this course to anyone who has an interest in Vision Transformers and wants to build strong foundational and practical knowledge in this area.

Lalatendu Sahu

AI Professional

Pricing

Invest in Your AI Skills

Choose the plan that fits your learning goals.

Free

₹0

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  • Access to lecture videos
Most Popular

Engineer

₹30,000

Everything you need to master context engineering.

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  • Access to lecture videos
  • Community forum access
  • Complete code files
  • Hand-written notes
  • PDF booklets
  • Session recordings
  • All additional lecture materials
  • Course certificate

Industry Professional

₹95,000

For professionals targeting AI research publication.

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  • Everything in Engineer
  • 2 months personalized research guidance
  • Custom research problem statement
  • Research project roadmap document
  • Guidance toward publishable manuscript
  • Targeting leading AI conferences

Enterprise

Custom

Bulk enrollment for teams and organizations.

Contact Us
  • Everything in Industry Professional
  • Bulk team enrollment
  • Invoice-based payment
FAQ

Frequently Asked Questions

Everything you need to know about the workshop.

Ready to Master Context Engineering?

Join the next cohort starting March 9 and learn the discipline that separates production AI from toy demos.