AI for Techies
Green Belt Program
A 40–50 hour code-first program for developers, engineers, and tech leads. Build, integrate, and deploy AI-enabled applications and agentic workflows — safely and responsibly, without needing enterprise-scale architecture.
- 40–50 Hours | 6 Modules
- Code-First | Labs Every Module
- AI Green Belt – IT Certified
- Prerequisite: AI Yellow Belt
6 Modules. Real Code.
Production-Ready Output.
agentic AI workflows — with hands-on labs, real tools, and a portfolio-ready capstone.
Generative AI Fundamentals for Developers
Purpose: Give developers enough LLM understanding to build reliable applications — not train models.
A. Core LLM Concepts
- How LLMs work — tokens, context windows, temperature, and sampling
- Prompt vs. system vs. tool instructions — how each shapes model behaviour
- Determinism vs. creativity — controlling outputs for production use
- Hallucinations, failure modes, and where LLMs should NOT be used
B. Model Selection Framework
- When to use small vs. large models, speed vs. accuracy trade-offs
- Cost vs. performance decision matrix across OpenAI, Anthropic, Gemini, and open-source
- Lab: Work on 5 real-world tasks — choose the right model and compare output quality, latency, and cost
C. Multi-Modal Models — Apps That See, Hear & Read
- Handle images (invoices, screenshots, reports), documents (PDFs, slides), and audio (voice → text → insights
- Build voice-enabled applications and automated document processing workflows
- Lab: Build an invoice pipeline — Input: image → Output: structured JSON (vendor, GST, line items, total) with validation
Building AI Apps — Prompts, Tools & Patterns
Purpose: Learn to build real AI-powered applications — not just write prompts.
A. Prompt Engineering for Production
- Design reusable prompts — not one-time queries — with system prompts, output format constraints, and few-shot examples
- Optimise prompts for cost and performance at scale
B. Tool Calling & Function Execution
- Enable AI to call functions and APIs — move from chatbot to functional AI systems
- Generate structured JSON outputs with schema control for reliable, parseable, production-ready responses
C. Error Handling & UX Patterns
- Handle retries, timeouts, and token limits gracefully
- Design streaming responses, fallbacks, and output validation layers
D. PromptOps — Managing Prompts Like Code
- Version and manage prompts like production code with reusable templates
- Test prompts to prevent breaking outputs — simulate bad changes and catch regressions
- A/B test prompts using data-driven experimentation, monitor performance and cost in real-time
RAG — Retrieval & Grounding (Applied)
Purpose: Connect AI with real data and documents so outputs are accurate, reliable, and verifiable — not hallucinated.
A. Core Concepts
- Why RAG: AI models don't know your latest data or internal documents — RAG fixes this by retrieving then generating
- Semantic search — how AI finds relevant information using embeddings and vector databases
- How to ensure answers are based on real, citable sources
B. Building RAG Systems
- Ingest and process documents (PDFs, reports, data) — structure data for better retrieval
- Design pipelines for accurate, fast responses using LangChain and LlamaIndex
- Multi-modal RAG: work with text, tables, and images in the same pipeline
C. Hands-On Labs
- Build a RAG-based AI system from scratch on real documents and queries
- Test retrieval accuracy and iteratively improve pipeline quality
Agentic AI — Agents, Tools & Orchestration
Purpose: Build AI systems that can plan, use tools, take actions, and stay controlled in real-world applications.
A. What You Learn
- What AI agents are and how they differ from fixed pipelines
- How agents break complex tasks into steps, choose and use tools intelligently
- How memory, state, and decision-making work in agent-based systems
B. Controlled Agent Design
- When to use agents vs. fixed pipelines — reliability trade-offs
- Add human approval checkpoints before important actions
- Design safe systems: limits, checks, oversight, and guardrails
C. Hands-On Labs
- Build a multi-step agent workflow with tool-using agents for real tasks
- Add approval, escalation, and fallback logic
- Test how agents behave in unexpected or edge-case situations
LLM Testing & Evaluation at Scale
Purpose: Ensure AI systems are accurate, reliable, and production-ready — not just working in demos.
A. Why Evaluation Matters
- AI that "looks correct" can still be wrong — small changes break outputs silently
- Production systems need measurable quality, not guesswork or manual review
B. What You Measure
- Accuracy & correctness, relevance & completeness
- Hallucination / faithfulness scoring, safety & output format compliance
- Performance, latency, and cost metrics across prompt and model changes
C. Evaluation in Practice
- Build evaluation datasets that reflect real-world use cases
- Compare outputs using automated evaluation methods at scale
- Simulate failures, detect regressions, and continuously monitor quality
Capstone Project — AI App or Agentic Workflow
Purpose: Build, present, and defend a deployable or demo-ready AI system.
A. Choose Your Track
- AI-enabled SaaS feature
- RAG-based internal knowledge assistant
- Agentic workflow for business automation
- AI-powered developer productivity tool
B. Capstone Must Demonstrate
- Clean LLM integration with appropriate model selection
- Grounding (RAG) or tool usage for reliable outputs
- Error handling and safety safeguards
- Human-in-the-loop control where required
- Deployable or demo-ready working system
Track Your AI Readiness Journey
execution capability.
Jointly Awarded by IIIT Delhi Innovation
and Incubation Center
& Skills Caravan
co-branded certificate recognised by top engineering and product organisations across India and globally.

Learn From Practitioners
India's leading product companies, cloud platforms, and research institutions.

FounderVIHU AI, EX – AWS

IIT Jammu

Founder Amquest Education,
Ex-CTO CoutLoot
Why Engineering Teams Choose Us
Code-First, Production-Grade
Built for Engineering Enterprises
Safe & Responsible AI by Design
Live Analytics & Tracking
Gamification & Peer Learning
Loved by Industry Leaders
Course Details & Pricing
or bulk nominations for your engineering team.
AI for IT — Green Belt
- 40–50 Hours across 6 modules
- Code-first, labs every session
- Python, LangChain, LlamaIndex
- OpenAI / Anthropic / Gemini APIs
- RAG pipeline you can deploy
- Multi-step agent workflow
- LLM evaluation & testing system
- AI Green Belt – IT Certificate
Team & Enterprise Enrollment
- Minimum 5 nominations
- Dedicated L&D dashboard
- Progress tracking & reporting
- Branded certificates available
- Volume discount pricing
- Dedicated program manager
Bulk discounts from 5+ seats
Empower Your Engineering Team with
Production AI Skills
Start building AI features that are reliable, grounded, and production-ready.
Course details and Pricing
Frequently Asked Questions
the stack, curriculum, certification, and what you walk away with.
Who is the AI for IT Green Belt program designed for?
What is the tech stack and what will I actually build?
What certification do I receive and how is it recognised?
What is RAG and why does every AI developer need to learn it?
What is agentic AI and how is it different from building a chatbot?
Why is LLM testing and evaluation its own module?
What does PromptOps mean and why should developers care?
What are the capstone project options and what makes a strong submission?
6 Modules. Real Code.
Production-Ready Output.
agentic AI workflows — with hands-on labs, real tools, and a portfolio-ready capstone.
Generative AI Fundamentals for Developers
Purpose: Give developers enough LLM understanding to build reliable applications — not train models.
A. Core LLM Concepts
- How LLMs work — tokens, context windows, temperature, and sampling
- Prompt vs. system vs. tool instructions — how each shapes model behaviour
- Determinism vs. creativity — controlling outputs for production use
- Hallucinations, failure modes, and where LLMs should NOT be used
B. Model Selection Framework
- When to use small vs. large models, speed vs. accuracy trade-offs
- Cost vs. performance decision matrix across OpenAI, Anthropic, Gemini, and open-source
- Lab: Work on 5 real-world tasks — choose the right model and compare output quality, latency, and cost
C. Multi-Modal Models — Apps That See, Hear & Read
- Handle images (invoices, screenshots, reports), documents (PDFs, slides), and audio (voice → text → insights
- Build voice-enabled applications and automated document processing workflows
- Lab: Build an invoice pipeline — Input: image → Output: structured JSON (vendor, GST, line items, total) with validation
Building AI Apps — Prompts, Tools & Patterns
Purpose: Learn to build real AI-powered applications — not just write prompts.
A. Prompt Engineering for Production
- Design reusable prompts — not one-time queries — with system prompts, output format constraints, and few-shot examples
- Optimise prompts for cost and performance at scale
B. Tool Calling & Function Execution
- Enable AI to call functions and APIs — move from chatbot to functional AI systems
- Generate structured JSON outputs with schema control for reliable, parseable, production-ready responses
C. Error Handling & UX Patterns
- Handle retries, timeouts, and token limits gracefully
- Design streaming responses, fallbacks, and output validation layers
D. PromptOps — Managing Prompts Like Code
- Version and manage prompts like production code with reusable templates
- Test prompts to prevent breaking outputs — simulate bad changes and catch regressions
- A/B test prompts using data-driven experimentation, monitor performance and cost in real-time
RAG — Retrieval & Grounding (Applied)
Purpose: Connect AI with real data and documents so outputs are accurate, reliable, and verifiable — not hallucinated.
A. Core Concepts
- Why RAG: AI models don't know your latest data or internal documents — RAG fixes this by retrieving then generating
- Semantic search — how AI finds relevant information using embeddings and vector databases
- How to ensure answers are based on real, citable sources
B. Building RAG Systems
- Ingest and process documents (PDFs, reports, data) — structure data for better retrieval
- Design pipelines for accurate, fast responses using LangChain and LlamaIndex
- Multi-modal RAG: work with text, tables, and images in the same pipeline
C. Hands-On Labs
- Build a RAG-based AI system from scratch on real documents and queries
- Test retrieval accuracy and iteratively improve pipeline quality
Agentic AI — Agents, Tools & Orchestration
Purpose: Build AI systems that can plan, use tools, take actions, and stay controlled in real-world applications.
A. What You Learn
- What AI agents are and how they differ from fixed pipelines
- How agents break complex tasks into steps, choose and use tools intelligently
- How memory, state, and decision-making work in agent-based systems
B. Controlled Agent Design
- When to use agents vs. fixed pipelines — reliability trade-offs
- Add human approval checkpoints before important actions
- Design safe systems: limits, checks, oversight, and guardrails
C. Hands-On Labs
- Build a multi-step agent workflow with tool-using agents for real tasks
- Add approval, escalation, and fallback logic
- Test how agents behave in unexpected or edge-case situations
LLM Testing & Evaluation at Scale
Purpose: Ensure AI systems are accurate, reliable, and production-ready — not just working in demos.
A. Why Evaluation Matters
- AI that "looks correct" can still be wrong — small changes break outputs silently
- Production systems need measurable quality, not guesswork or manual review
B. What You Measure
- Accuracy & correctness, relevance & completeness
- Hallucination / faithfulness scoring, safety & output format compliance
- Performance, latency, and cost metrics across prompt and model changes
C. Evaluation in Practice
- Build evaluation datasets that reflect real-world use cases
- Compare outputs using automated evaluation methods at scale
- Simulate failures, detect regressions, and continuously monitor quality
Capstone Project — AI App or Agentic Workflow
Purpose: Build, present, and defend a deployable or demo-ready AI system.
A. Choose Your Track
- AI-enabled SaaS feature
- RAG-based internal knowledge assistant
- Agentic workflow for business automation
- AI-powered developer productivity tool
B. Capstone Must Demonstrate
- Clean LLM integration with appropriate model selection
- Grounding (RAG) or tool usage for reliable outputs
- Error handling and safety safeguards
- Human-in-the-loop control where required
- Deployable or demo-ready working system
Track Your L&D Success
execution capability — jointly awarded by IIIT Delhi and Skills Caravan.
Jointly Awarded by IIT Delhi
& Skills Caravan
co-branded certificate recognised by top engineering and product organisations across India and globally.

Learn From Practitioners
India's leading product companies, cloud platforms, and research institutions.

Arjun Krishnan

Sneha Pillai

Rahul Gupta
Why Engineering Teams Choose Us
Code-First, Production-Grade
Built for Engineering Enterprises
Safe & Responsible AI by Design
Live Analytics & Tracking
Gamification & Peer Learning
Partner Ecosystem
Loved by Industry Leaders
Course details and Pricing
Course Details & Pricing
or bulk nominations for your engineering team.
AI for IT — Green Belt
- 40–50 Hours across 6 modules
- Code-first, labs every session
- Python, LangChain, LlamaIndex
- OpenAI / Anthropic / Gemini APIs
- RAG pipeline you can deploy
- Multi-step agent workflow
- LLM evaluation & testing system
- AI Green Belt – IT Certificate
Team & Enterprise Enrollment
- Minimum 5 nominations
- Custom cohort scheduling
- Dedicated L&D dashboard
- Progress tracking & reporting
- Branded certificates available
- LMS / HRMS integration
- Volume discount pricing
- Dedicated program manager
Bulk discounts from 5+ seats
Empower Your Engineering Team with
Production AI Skills
Start building AI features that are reliable, grounded, and production-ready.
Frequently Asked Questions
the stack, curriculum, certification, and what you walk away with.









