Orientation to Computing — I
Unit 5: Version Control, Modern AI Trends & Profile Creation
From your first git push to crafting AI prompts to building a recruiter-ready developer identity — the skills that get you hired.
🏢 Industry-Aligned | 📝 15 MCQs (Bloom's Taxonomy) | 🔬 5 Lab Exercises | 💼 Interview & Career Prep
Why This Chapter Changes How You Think About Computing
This is the chapter that directly determines whether you get hired. Not theory exams. Not CGPA alone. Your GitHub profile, your ability to use AI tools intelligently, and your presence on coding platforms — these are what recruiters look at in 2025. At TCS, Infosys, and every startup, the first interview question is increasingly: "Show me your GitHub."
🏢 Industry Snapshot — Who Uses This Knowledge Daily?
Razorpay — Every developer pushes code via Git multiple times daily. Pull requests, code reviews, branch protection, CI/CD pipelines — their entire engineering culture is built on Git. A developer who can't use Git is like a driver who can't steer.
Google / DeepMind — Built Gemini, one of the most powerful AI models. Every Google product — Search, Maps, YouTube, Gmail — uses AI. Understanding prompt engineering and AI capabilities isn't optional for any tech career in 2025.
TCS / Infosys / Wipro — Campus placement processes now include HackerRank tests, GitHub profile reviews, and AI tool awareness questions. 83% of Indian IT companies check candidates' online coding profiles before shortlisting.
Prerequisite Checklist ✅
- ✅ Basic computer usage (you've saved files, used a browser)
- ✅ Awareness of what programming is (from Unit 2)
- ✅ Access to a computer with internet connection
- ✅ No prior Git, AI, or platform experience required
Learning Outcomes — Bloom's Taxonomy
| Bloom's Level | Learning Outcome |
|---|---|
| L1 — Remember | List the core Git commands (init, add, commit, push, pull, branch, merge) and name 5 types of generative AI tools |
| L2 — Understand | Explain the Git workflow (working directory → staging → commit → remote) and describe how LLMs generate text using token prediction |
| L3 — Apply | Create a GitHub repository, push code with proper commit messages, and write effective AI prompts using few-shot and chain-of-thought techniques |
| L4 — Analyze | Compare ChatGPT vs Claude vs Gemini for specific tasks and differentiate between good and poor prompts with reasoning |
| L5 — Evaluate | Justify the ethical use of AI in academic work and evaluate when AI-generated code is assistance vs plagiarism |
| L6 — Create | Design a complete developer profile strategy (GitHub README, HackerRank certifications, LeetCode plan) and build a portfolio on GitHub Pages |