Artificial Intelligence & Prompt Engineering

Week 1: Artificial Intelligence Foundations

Establishes conceptual clarity and practical orientation to AI.

AI fundamentals

• What is AI? History, evolution & current landscape

• AI vs ML vs Deep Learning vs Generative AI

• Narrow AI vs General AI

• Real-world AI applications across industries

Core AI concepts

• Data, algorithms & models

• Supervised, unsupervised & reinforcement learning (overview)

• Rule-based systems vs learning systems

AI tools ecosystem

• Overview of modern AI platforms

• Open-source vs proprietary AI tools

• Cloud-based AI services (conceptual)

Hands-on

• Exploring AI-powered tools for text, images & productivity

• Case study analysis of AI-driven products

Week 2: Generative AI & Large Language Models (LLMs)

Builds understanding of modern generative systems and their capabilities.

Generative AI basics

• What is generative AI?

• Text, image, audio & video generation

• Foundation models & multimodal AI

Large Language Models

• How LLMs work (tokens, embeddings, transformers – conceptual)

• Pre-training, fine-tuning & inference

• Limitations: hallucinations, bias, context windows

Popular LLM platforms

• Chat-based AI systems

• Open-source LLMs overview

• API vs UI-based usage

Hands-on

• Using LLMs for summarization, content creation, coding assistance

• Prompt-based experimentation

Week 3: Prompt Engineering Techniques

Introduces structured prompting for reliable and high-quality AI outputs.

Prompt engineering fundamentals

• Anatomy of a prompt

• Zero-shot, one-shot & few-shot prompting

• Instruction-based prompting

Advanced prompting techniques

• Chain-of-thought prompting

• Role-based prompts

• Context injection & constraints

• Output formatting & validation

Task-specific prompting

• Prompts for coding

• Prompts for data analysis

• Prompts for marketing & content

• Prompts for education & research

Hands-on

• Design prompt templates for real-world tasks

• Prompt optimization & iteration

Week 4: AI Workflows, Automation & Ethics

Elevates to applied AI workflows and responsible usage.

AI-assisted workflows

• AI for productivity & automation

• Integrating AI into daily workflows

• No-code / low-code AI tools

Prompt-driven automation

• Multi-step prompt workflows

• Tool-augmented prompting

• AI agents (conceptual overview)

Ethics & governance

• Bias & fairness in AI

• Data privacy & security

• Responsible AI usage

• AI regulations & future outlook

Hands-on

• Build an AI-assisted workflow

• Ethical evaluation of AI use cases

Week 5: Applied AI Projects & Capstone

Concludes with practical implementation and career readiness.

Applied AI use cases

• AI for business decision-making

• AI in education, healthcare & finance

• AI for software development & design

Prompt engineering in production

• Prompt libraries & versioning

• Measuring output quality

• Reducing hallucinations

• Human-in-the-loop systems

Capstone project

• Design a domain-specific AI assistant

• Define problem → design prompts → test & refine

• Use-case documentation & presentation

Career & future readiness

• AI-augmented roles

• Portfolio building

• Freelancing & consulting with AI tools

Tools & Platforms Covered

• Chat-based AI platforms

• Open-source LLMs (overview)

• Prompt libraries & workflow tools

• AI productivity & automation tools

Learning Outcomes

• Understand AI & generative AI fundamentals

• Design effective prompts for diverse tasks

• Build AI-assisted workflows & assistants

• Apply ethical and responsible AI practices

• Prepare for AI-augmented careers

NB: Time and slot will vary according to availability of mentor, minimum mentee count, and company policies.