AKTU Introduction to AI & Prompt Engineering Syllabus 2026-27 (AAI101/AAI201) — Full Unit-Wise Breakdown

Out of every new subject added to the AKTU first-year syllabus this year, this is the one I get the most messages about — mostly from students (and a few worried parents) asking "wait, is this a coding subject or not?" Short answer: no, it isn't. Let me actually walk you through it properly, because the confusion is understandable and the course itself is genuinely well thought out once you see the full breakdown.

Introduction to AI & Prompt Engineering is one of the new Engineering Science Courses added to the AKTU B.Tech First Year CSE Stream syllabus for the 2026-27 academic session, applicable across almost every computer-related branch under the CSE Stream. It carries course code AAI101 (Semester I) / AAI201 (Semester II) depending on which semester your college offers it in, since it appears as an alternative option alongside Fundamentals of Mechanical Engineering in the official scheme.

Quick Facts: AKTU AI & Prompt Engineering Syllabus 2026-27

The course description in the official document is refreshingly direct about why this subject exists at all: AI is treated as the defining technology of this decade, and every engineer — regardless of branch — is expected to work alongside AI tools throughout their career. That's the actual justification AKTU gives, and honestly, it's hard to argue with it in 2026.

What You'll Actually Learn (Unit-Wise Breakdown)

Unit I: Introduction to Artificial Intelligence (20% weightage)

This opening unit covers the definition and scope of AI, a brief history running from Alan Turing to ChatGPT, and the difference between Narrow AI, General AI, and Super AI at a concept level. You'll also cover the distinction between AI, Machine Learning, and Deep Learning, how machines learn from data and patterns without diving into heavy maths, and where AI already sits in daily life — Google Maps, Spotify, Netflix, Google Photos, voice assistants. It also introduces supervised, unsupervised, and reinforcement learning as learning strategies, along with basic data visualization and pre-processing concepts. A nice touch here: the class activity asks students to list 5 AI tools they already use daily and figure out how each one "learns," with branch-specific connections drawn for CE, ME, EE, CSE, BT, and AG students separately.

Unit II: Core AI Concepts (20% weightage)

This is where things get slightly more technical, though still concept-first. You'll cover classification techniques like K-Nearest Neighbors and Naïve Bayes, the K-Means clustering technique, and classification metrics — accuracy, precision, recall, F1 score, and MAE. Neural networks are introduced through the basic idea of neurons, layers, and weights using simple diagrams, followed by what actually makes deep learning "deep" and why it needs GPUs. Computer vision and Natural Language Processing round out the unit, with a fun hands-on activity using Google's Teachable Machine to train a simple image classifier in five minutes with zero code.

Unit III: Generative AI and Large Language Models (20% weightage)

Given how much of 2026's tech conversation revolves around this exact topic, this unit is probably the most relevant to what students will encounter outside the classroom too. It covers what Generative AI actually is, how LLMs are trained at a conceptual level, and a direct comparison of the major players — ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), and Llama (Meta). You'll also learn what tokens, context windows, and temperature mean in simple terms, get introduced to multimodal AI, and — importantly — cover the limitations of LLMs including hallucination, knowledge cutoffs, and bias, plus responsible use around academic integrity and AI-content disclosure. The class activity has students interact directly with an LLM and identify a real hallucination example, which is a genuinely useful exercise for building healthy skepticism early.

Unit IV: AI Applications Across Engineering Domains (20% weightage)

This unit is built to make sure the subject feels relevant no matter which branch you're actually in. It walks through AI in Civil Engineering (structural health monitoring, smart cities), Mechanical Engineering (predictive maintenance, robotics), Electrical and Electronics (smart grids, VLSI design automation), CSE (code generation, cybersecurity), Biotechnology and Healthcare (drug discovery, medical imaging), Agricultural Engineering (precision farming, drone-based spraying), and Chemical/Textile Engineering (process optimization, quality control). It closes with a section on ethical concerns — bias, data privacy, job displacement — through a group case-study presentation format.

Unit V: Prompt Engineering (20% weightage)

This is the unit that gives the course its name, and it's entirely hands-on. You'll learn what a prompt actually is, why prompt quality changes results so dramatically, and the core principles of good prompting — clarity, context, specificity, role assignment, and format instructions. The prompting techniques covered are the ones that matter most in practice: zero-shot prompting, few-shot prompting, chain-of-thought prompting, and role prompting (the classic "act as a civil engineer and..." style instruction). You'll also practice prompting for different tasks like summarization, code generation, report writing, and translation, plus domain-specific prompting tailored to your own branch. The graded assignment for this unit asks you to write five prompts for an engineering problem from your own field — a genuinely practical, resume-relevant skill by the time you're done.

Evaluation Pattern

Assignments You Should Expect

The course includes six suggested assignments spread across the five units — a research task identifying real AI applications in your branch, a concept-check on supervised vs. unsupervised learning, a hands-on hallucination-identification exercise with a real LLM, a group case study presentation, a graded prompt-writing assignment covering all five prompting techniques, and a reflection task on using AI for an academic task while documenting what you verified or corrected. None of these require heavy technical background — the emphasis throughout is on understanding and application, not building models from scratch.

Recommended Books and Free Resources

For textbooks, the official list includes The Quick Guide to Prompt Engineering by I. Khan, AI: Its Nature and Future by Margaret Boden, Human Compatible by Stuart Russell, and Prompt Engineering for Generative AI by James Phoenix and Mike Taylor. For free online resources, Google's Teachable Machine, Elements of AI (a well-known free beginner course from the University of Helsinki), and promptingguide.ai are all worth bookmarking well before your exam approaches.

Why This Subject Is Actually Worth Your Attention

I'll be honest — when a university adds a brand-new subject, students often assume it's filler. This one genuinely isn't. The syllabus itself notes that prompt engineering is already a recognized job role with average global salaries north of $100K, and regardless of whether you end up in a role with that exact title, being able to communicate effectively with AI tools is quickly becoming a baseline expectation in almost every engineering career path. Treat the Unit V assignments seriously — that's the part of this course that will actually follow you into internships and placements.

Frequently Asked Questions

Is Introduction to AI & Prompt Engineering a coding subject?

No. The course explicitly requires no prior programming or mathematics knowledge. It's concept- and application-focused, taught through analogies, diagrams, demonstrations, and hands-on prompting practice rather than coding assignments.

What is the course code for AKTU AI & Prompt Engineering?

The course code is AAI101 for Semester I offering and AAI201 for Semester II offering, depending on how your specific branch's course structure schedules it.

How many credits is AI & Prompt Engineering worth?

It carries 2 credits and 30 NCrF hours, categorized as an Engineering Science Course (ESC).

What AI tools are used in this course?

The syllabus specifies free-tier, browser-based tools: ChatGPT, Claude, Gemini, and Copilot. No paid subscriptions or installations are required.

What topics does Unit V (Prompt Engineering) cover?

Unit V covers zero-shot prompting, few-shot prompting, chain-of-thought prompting, role prompting, domain-specific prompting, and practical prompting for tasks like summarization, code generation, and report writing.

Is this subject the same in every branch under the CSE Stream?

The course structure and content are common across the CSE Stream, but it's offered as an alternative to Fundamentals of Mechanical Engineering in the official scheme — check your specific branch's course list to confirm which one applies to you.

For the complete first-year picture, head back to the AKTU B.Tech 1st Year Syllabus 2026-27 overview, or check the official syllabus PDF for the full unit-wise document.