🚀 TECH 101 – FOUNDATION ENGINEER
Program Title: AI-Augmented Engineer
Duration: 12 Sessions (2 Hours Each)
🎯 Program Objective
The objective of TECH 101 is to enable early-career engineers to effectively use Generative AI tools to accelerate coding, debugging, documentation, and learning workflows while maintaining accuracy, validation discipline, and responsible AI usage practices.
Participants will learn how to leverage AI as a productivity multiplier rather than a shortcut, enabling them to deliver features faster with improved clarity and confidence.
Participants will learn how to leverage AI as a productivity multiplier rather than a shortcut, enabling them to deliver features faster with improved clarity and confidence.
✅ Pre-Requisites
- Basic understanding of programming concepts and at least one programming language
- Familiarity with Git-based workflows and development environments
- Foundational knowledge of the software development lifecycle
- No prior AI knowledge required
📘 Curriculum Overview
| Session | Topic | Detailed Coverage | Tools | Learning Outcome |
|---|---|---|---|---|
| 1 | GenAI Basics | LLMs, tokens, context windows, probabilistic outputs, AI vs retrieval | ChatGPT | Understand how GenAI works in engineering workflows |
| 2 | GenAI Use Cases | AI across coding, debugging, documentation & learning | Copilot | Identify practical daily AI use cases |
| 3 | Prompt Fundamentals | Role, context, constraints, output format structuring | ChatGPT | Write structured prompts effectively |
| 4 | Prompt Techniques | Zero-shot, few-shot, structured outputs (JSON, tables) | ChatGPT | Improve AI response quality systematically |
| 5 | Prompt Debugging | Refining ambiguous inputs, diagnosing weak prompts | ChatGPT | Fix poor AI outputs consistently |
| 6 | AI Coding Basics | Generate functions, boilerplate, requirement-to-code conversion | GitHub Copilot | Generate working code efficiently |
| 7 | AI Debugging | Error interpretation, root cause analysis, AI-assisted fixes | Copilot | Reduce debugging time significantly |
| 8 | Code Understanding | Explain legacy code, refactoring suggestions, logic flow | Copilot | Understand unfamiliar codebases faster |
| 9 | AI Learning | Concept breakdown, structured explanations, learning paths | ChatGPT | Accelerate independent learning |
| 10 | AI Documentation | Generate summaries, READMEs, technical documentation | Microsoft Copilot | Create documentation faster & cleaner |
| 11 | Responsible AI | Hallucination detection, validation discipline, safe usage | ChatGPT | Validate AI outputs responsibly |
| 12 | Case Study | Build small feature using AI for coding, debugging & documentation | All Tools | Deliver complete AI-assisted feature |