Introduction to AI and Prompt Engineering (AAI101) — Notes & Syllabus — AKTU
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Introduction to AI and Prompt Engineering — Question Bank
Ten long questions from every unit — the ones that keep coming back — plus short-answer questions covering the whole syllabus for the compulsory first question. Answers are in the complete notes for this paper.
Introduction to AI and Prompt Engineering — Complete Notes
Every unit of the AAI101 / AAI201 syllabus, written against the official scheme: Unit I Introduction to Artificial Intelligence; Unit II Core AI Concepts; Unit III Generative AI and Large Language Models; Unit IV AI Applications Across Engineering Domains; Unit V Prompt Engineering. Worked examples, tables and diagrams throughout, with the high-yield points marked.
Syllabus — Introduction to AI and Prompt Engineering (AAI101)
Official AKTU syllabus, effective from the academic session 2026-27 (AICTE model curriculum / NEP 2020).
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Course objectives
Understand what Artificial Intelligence is, its history, and how it is changing every engineering field 2. Understand key AI concepts — Machine Learning, Deep Learning, and Neural Networks — at a conceptual level 3. Explore how AI is applied across domains — healthcare, agriculture, manufacturing, finance, and education 4. Understand Generative AI — how large language models (LLMs) like ChatGPT and Gemini work 5. Learn and practise Prompt Engineering — the skill of communicating effectively with AI tools to get useful results
Course content
Definition and scope of AI; brief history — from
Alan Turing to ChatGPT
Types of AI — Narrow AI, General AI, Super AI (concept)
AI vs Machine Learning vs Deep Learning — what is the difference
How machines learn — data, patterns, and predictions (concept, no maths)
AI today — where it already exists in daily life (Google Maps, Spotify, Netflix, Google Photos, voice assistants)
Data, Data Visualization, Data Pre-processing Class Activity: Students list 5 AI tools they use every day and discuss how each one 'learns'. Branch connect: CE — AI in structural health monitoring; ME — AI in predictive maintenance; EE — smart grid; CSE — AI in compilers; BT — drug discovery; AG — crop yield prediction. Core AI Concepts
Classification metrics-accuracy, precision, recall, F1 score, MAE.
Neural networks — concept of neurons, layers, weights (simple diagram)
Deep learning — what makes it 'deep'; why it needs GPUs, CNN
Computer vision — how AI sees images (face recognition, medical imaging)
Natural Language Processing (NLP) — how AI understands text and speech Class Activity: Show Google Teachable Machine (teachablemachine.withgoogle.com) — train a simple image classifier in 5 minutes, no code. Branch connect: ECE — image sensors + AI; BT — protein structure prediction (AlphaFold); AG — plant disease detection from photos; TX — fabric defect detection. Generative AI and Large Language Models
What is Generative AI — AI that creates text, images, code, audio, video
Large Language Models (LLMs) — what they are, how they are trained (concept)
Popular LLMs — ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), Llama (Meta)
Tokens, context window, temperature — simple explanation
Multimodal AI — text + image + voice in one model (GPT-4o, Gemini concept)
Limitations of LLMs — hallucination, knowledge cutoff, bias
Responsible use — academic integrity, plagiarism, AI-generated content disclosure Class Activity: Students interact with ChatGPT/Gemini/Claude; observe how different inputs give different outputs; identify one hallucination example. Every professional today uses at least one Generative AI tool. Understanding how LLMs work makes students better users and future developers. AI Applications Across Engineering Domains
AI in Civil Engineering — structural health monitoring, smart cities, traffic optimisation
AI in Mechanical Engineering — predictive maintenance, design optimisation, robotics
AI in Electrical and Electronics — smart grids, fault detection, VLSI design automation
AI in CSE — code generation (GitHub Copilot), cybersecurity, natural language interfaces
AI in Biotechnology and Healthcare — drug discovery, medical imaging, genomics, wearables
AI in Agricultural Engineering — precision farming, crop monitoring, drone-based spraying, soil analysis
AI in Chemical and Textile Engineering — process optimisation, quality control, material design
Ethical concerns — bias in AI, data privacy, job displacement, responsible AI Class Activity: One case study per student group — each group presents a 2-minute real AI application from their own engineering branch. This unit ensures every student, regardless of branch, sees AI as directly relevant to their future career. Prompt Engineering
What is a prompt — the instruction you give to an AI model
Why prompt quality matters — same AI, very different results with different prompts
Principles of good prompting — clarity, context, specificity, role assignment, format instruction
Prompting techniques: • Zero-shot prompting — direct question • Few-shot prompting — give examples in the prompt • Chain-of-thought prompting — ask AI to think step by step • Role prompting — 'Act as a civil engineer and...'
Prompt for different tasks — summarisation, explanation, code generation, report writing, brainstorming, translation
Domain-specific prompting — students write prompts relevant to their own branch (CE, ME, BT, AG, etc.)
AI tools for engineers — GitHub Copilot (code), Grammarly (writing), Consensus (research), Wolfram Alpha, Canva AI, Notion AI Hands-on Practice (every lecture): Students write prompts, compare outputs, refine and improve. Graded prompt assignment: write 5 prompts for an engineering problem from their own branch. Prompt Engineering is already a job role (avg. salary $100K+ globally). Every engineer who can communicate well with AI has a significant career advantage. Suggested Assignments / Practice Tasks but not limited to Assignment Task Research task — no AI tool interaction required. Find 3 real AI applications in your engineering branch. For each: Assignment name the product/system, what data it uses, and what it 1 (Unit I) predicts or generates. Submit as a one-page note (250– 300 words). Concept-check task. Pick one real ML-based system relevant to your branch (e.g., a recommendation engine, Assignment a defect detector, a chatbot). Identify: (a) Is it supervised, 2 (Unit II) unsupervised, or reinforcement learning? (b) What could cause it to overfit or underfit? (c) Sketch a simple diagram showing its inputs, a basic neuron/layer concept, and output. Submit as a half-page note (200–250 words) with diagram. Hands-on task — direct AI interaction required. Interact with any free LLM (ChatGPT/Gemini/Claude). Ask it a Assignment question related to your branch. Identify one correct and 3 (Unit III) one incorrect (hallucinated) response. Explain why the hallucination occurred (150–200 words), with screenshots of both responses. Group case study: Each group (3–4 students) picks one AI application in their domain, prepares a 2-minute Assignment presentation covering: problem → data → AI solution → 4 (Unit IV) impact. Submit slides/notes plus a 1-page group summary handout. Write 5 prompts for an engineering problem from your branch using: (a) Zero-shot (b) Few-shot (c) Chain-of- Assignment thought (d) Role prompting (e) Format-specific prompt. 5 (Unit V) Submit the prompt + AI response + your evaluation of the output quality, as a single document (2–3 pages). Use an AI tool to help complete one academic task (summarise a topic, generate a study plan, explain a concept, translate a text). Document the prompt used, Assignment output received, and how you verified/corrected it. In a 6 (Unit V) 200–300 word reflection, address: (a) what you changed or verified and why, and (b) one way AI use in this task could raise a bias, privacy, or academic-integrity concern.
Unit Topics Weightage Introduction to Artificial Intelligence