Artificial Intelligence and Prompt Engineering

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ARTIFICIAL INTELLIGENCE AND DI04000161
FREE PDF DI04000161
Code: DI04000161 Credits: 3 Semester 4 Electronics

Artificial Intelligence and Prompt Engineering

Get the full GTU study material, English & Gujarati textbook PDFs, syllabus curriculum, and solved previous year question papers.

Artificial Intelligence and Prompt Engineering Syllabus — Unit-wise Content

The official GTU curriculum for Artificial Intelligence and Prompt Engineering (subject code DI04000161) as prescribed for Electronics · Semester 4 divides the course into 5 units, covering 20 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.

Download the official syllabus PDF (DI04000161)

Unit 1 Introduction to AI & Generative AI

7 lecture hours

  • 1.1 Definitions: Intelligence
  • Artificial Intelligence
  • Machine Learning 1.2 Evolution of AI: From Rule-based systems to Generative AI. 1.3 Types of AI: Narrow AI (ANI) vs
  • General AI (AGI). 1.4 Concept of Generative AI: How text and image generators work (prediction logic vs. search engine). 1.5 Common AI Tools overview: ChatGPT

Unit 2 Generative Models & Tools

8 lecture hours

  • Concepts and Safe Use 2.1 How LLMs behave (tokens
  • instructions—no coding) 2.2 Types of AI tools: chat LLMs
  • multimodal tools
  • assistants 2.3 Responsible AI use: privacy
  • ethics 2.4 Selecting the right AI tool for a task (accuracy
  • limitations) 2.5 Understanding bias and fairness in generative models
  • including strategies to identify
  • mitigate, and monitor potential biases in AI systems

Unit 3 Prompt Engineering

10 lecture hours

  • Foundations 3.1 What is a prompt? Structure and components  Instruction  Context  Constraints  Examples  Output format 3.2 Types of prompts  Role prompts  Instruction prompts  Task prompts  Information extraction prompts w.e.f.2025-26 http://syllabus.gtu.ac.in/ Pageno.3 of 7 Program Name: Diploma Engineering Level: Diploma Branch: Automobile Engineering / Mechanical Engineering /Mechanical Engineering (CAD/CAM)/ Civil Engineering / Environmental Engineering / Mechatronics Engineering / Mining Engineering /Fabrication Technology Subject Code : DI04000161 Subject Name: Artificial Intelligence and Prompt Engineering 3.3 Core prompting strategies  Step-by-step prompting  Clarification prompts  Decomposition of complex tasks  Simple few-shot prompting 3.4 Building prompt templates  Report template  Checklist template  SOP template 3.5 Evaluating prompt output quality  Accuracy  Clarity  Completeness  Verifiability

Unit 4 Engineering

11 lecture hours

  • Advanced Methods & Practical Applications (Non-Coding) 4.1 Advanced prompting techniques  Scaffolding prompts  Prompt chaining & iterative refinement  Meta-prompts (prompts about prompts)  Constraint-based prompting (format
  • sections) 4.2 Critical evaluation & validation  Detecting hallucinations  Verification and reverse-prompting  Cross-checking with standards/manuals  Uncertainty prompts (“what info is missing?”
  • “assumptions?”) 4.3 Multimodal prompting (conceptual)  How to describe an image to an AI  How to ask for analysis (without relying on domain knowledge)  Preparing annotated checklists 4.4 Designing workflow-level prompts  Maintenance workflow prompts w.e.f.2025-26 http://syllabus.gtu.ac.in/ Pageno.4 of 7 Program Name: Diploma Engineering Level: Diploma Branch: Automobile Engineering / Mechanical Engineering /Mechanical Engineering (CAD/CAM)/ Civil Engineering / Environmental Engineering / Mechatronics Engineering / Mining Engineering /Fabrication Technology Subject Code : DI04000161 Subject Name: Artificial Intelligence and Prompt Engineering  Inspection workflow prompts  Safety workflow prompts 4.5 Building your own Prompt Library  Categorisation  Version control (simple documentation)  Sharing prompts within departments

Unit 5 Governance, Ethics, Security and Future Trends

9 lecture hours

  • 5.1 AI Ethics: Bias in data
  • Deep fakes, and Copyright issues. 5.2 Data Privacy: What not to share with public AI tools (Proprietary designs
  • Passwords). 5.3 The Future of Work: How AI changes the role of an Engineer (Human-in-the-loop). 5.4 Responsible AI usage in academia and industry. 5.5 Indian cyber regulations overview (non-legal
  • awareness level) 5.6 Capstone project: prompt-based solution to real engineering task

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