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KRIMKAR

Curriculum

Foundations first. Then specialise.

Every learner completes the pre-course, the foundation course, one specialisation track, the capstone and the paid internship.

Programme journey

Twelve months, five stages.

Each training month includes 18 hours of instructor-led class time. The internship begins once the 108 contact hours are complete.

  1. 01

    Pre-course

    Fundamentals of Computing

    How computers, operating systems, networks, the internet and data work — alongside digital tools and logical thinking.

    Open to all backgrounds

  2. 02–03

    Foundation

    AI + LLMs

    How AI and LLMs work, prompt engineering, working with data, and responsible AI: ethics, governance, safety and alignment.

    Common to both tracks

  3. 04–06

    Specialisation

    Choose your path

    Developer or Business track modules, with weekly labs and a capstone project.

    Developer or Business

  4. 07–12

    Paid internship

    Real AI projects

    Real AI projects with a host organisation, guided by a Krim mentor.

    ₹15,000 / month

  5. 12

    Certification

    Certificate in AI Application

    Awarded after the final examination and internship review.

    Final exam + review

Months 1–3 · Common to all learners

Start strong. Build responsibly.

Each training month includes 18 hours of instructor-led class time. Months 1–6 total 108 contact hours before the internship begins.

Month 1 · Pre-course

Fundamentals of Computing

No prior technical background needed. Brings every learner to the same starting line.

  1. 01How computers work: hardware, software, OS
  2. 02Files, cloud storage and digital workspaces
  3. 03Networks, the internet and cybersecurity basics
  4. 04Data, databases and spreadsheets
  5. 05Logic, algorithms and computational thinking
  6. 06Introduction to programming concepts

Months 2–3 · Foundation course

AI Foundations for all

Common to Developer and Business tracks, before learners specialise.

  1. 01How AI, machine learning and LLMs really work
  2. 02Prompt engineering and AI productivity
  3. 03Working with data using AI
  4. 04AI ethics and responsible use
  5. 05AI governance, policy and data privacy
  6. 06AI safety and alignment fundamentals

Responsible AI

Intelligence without responsibility is incomplete.

Responsible AI is taught as a core subject, not a footnote. Every learner, on either track, studies how to build and use AI that is fair, safe, accountable and aligned with human intent, and applies it in their capstone and internship.

  • AI Ethics
  • AI Governance
  • AI Safety
  • AI Alignment
  • AI Evaluation
  • Responsible Deployment
  • Human Oversight

  1. 01

    Model

    Understand what the system is and what it was trained to do.

  2. 02

    Evaluate

    Measure quality, bias and failure modes before trusting it.

  3. 03

    Validate

    Check it against policy, safety rules and the people it serves.

  4. 04

    Deploy

    Release with guardrails, documentation and a human in the loop.

  5. 05

    Monitor

    Watch real behaviour, review incidents and keep improving.

Ethics

Bias and fairness, transparency, explainability, accountability and the social impact of AI.

Governance

AI policies, risk assessment, documentation, compliance, and data protection under India’s DPDP Act.

Safety

Hallucinations, misuse and prompt-injection risks, red-teaming, guardrails and incident response.

Alignment

Keeping AI behaviour aligned with human intent: human oversight, evaluation, feedback and review loops.

Months 4–6 · Specialisation

Two tracks. One responsible AI foundation.

Choose one track. Both include weekly labs and a capstone project.

Developer

Basic Python needed; a free refresher is included.

  1. 01Python for AI3 cr
  2. 02AI Dataset Creation & Curation4 cr
  3. 03LLM APIs & RAG4 cr
  4. 04AI Agents & Tool Use3 cr
  5. 05Agent Operations, Review & Policy Compliance3 cr
  6. 06Deployment, Evaluation & Safety Testing3 cr

You’ll build: A curated dataset and a reviewed AI agent, deployed with a public link for your portfolio.

Developer track in detail

Business

No coding needed.

  1. 01Business Data Analytics with AI4 cr
  2. 02AI Product Management4 cr
  3. 03No-Code Automation3 cr
  4. 04AI for Marketing & Sales3 cr
  5. 05AI Strategy & ROI3 cr
  6. 06AI Policy & Governance in Business3 cr

You’ll build: An analytics dashboard and a costed AI product plan for a real business.

Business track in detail

Credits

A credit-based learning architecture.

A proposed total of 60 programme credits per learner.

Programme credits per learner
AreaModuleCredits
Pre-courseFundamentals of Computing4
FoundationAI & LLM Foundations4
FoundationPrompt Engineering & AI Productivity4
FoundationWorking with Data using AI4
FoundationAI Ethics, Governance, Safety & Alignment4
TrackDeveloper or Business specialisation (six modules)20
CoreCapstone Project4
IndustryPaid Industry Internship (6 months)16
TotalPre-course 4 + foundation 16 + track 20 + capstone 4 + internship 1660

Credit note. The 60-credit total is a proposed Academy programme structure and should not be represented as externally accredited academic credit unless the relevant accreditation or recognition is formally established.

Your AI career doesn’t start with a job. It starts with capability.

Build the foundations. Learn the systems. Work on real problems.

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Questions? Call +91 84476 13585 or write to admin@krimkar.com.

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