Generative AI Course in Kanpur

1.1 Introduction to AI

• What is Artificial Intelligence?
• AI vs. Automation vs. Analytics: Understanding the distinctions
• Types of AI: Narrow AI, General AI, and Super AI
• Real-world examples of AI in action

1.2 AI Family Tree

• AI, Machine Learning (ML), and Deep Learning (DL): Relationships and differences
• Supervised vs. Unsupervised vs. Reinforcement Learning (brief overview)
• Neural Networks: The building blocks

1.3 History & Evolution of AI

• Key milestones: From Turing Test to AlphaGo to ChatGPT
• AI winters and breakthroughs
• Current state of AI

1.4 Core AI Applications

• Natural Language Processing (NLP): chatbots, translation, sentiment analysis
• Computer Vision (CV): facial recognition, object detection, medical imaging
• Robotics & Autonomous Systems
• Predictive Analytics: forecasting, recommendation systems

1.5 AI Across Industries

• HR: Recruitment screening, employee engagement analysis
• Operations: Supply chain optimization, predictive maintenance
• Healthcare: Diagnosis assistance, drug discovery
• Retail: Personalization, inventory management
• Education: Adaptive learning, automated grading
• Finance: Fraud detection, algorithmic trading
• Manufacturing: Quality control, process automation

1.6 Future of AI

• Emerging trends: Multimodal AI, Edge AI, Quantum ML
• Opportunities: Productivity gains, innovation acceleration
• Risks: Job displacement, privacy concerns, weaponization

2.1 Introduction to Generative AI

• What is Generative AI? Definition and characteristics
• Traditional AI (predictive) vs. Generative AI (creative)
• Everyday GenAI applications: Writing assistants, image generators, code completion
• Popular GenAI tools: ChatGPT, Claude, Gemini, DALL-E, Midjourney

2.2 How GenAI Works

• Large Language Models (LLMs) explained simply
• Introduction to Transformers: Attention mechanism in plain language
• Tokens and tokenization
• Training vs. Fine-tuning vs. Inference
• Parameters and model size (7B, 70B, 405B – what do they mean?)

2.3 Key GenAI Models Overview

• OpenAI GPT family (GPT-4, GPT-4o, o1)
• Anthropic Claude (Sonnet, Opus, Haiku)
• Google Gemini
• Meta LLaMA
• Open-source vs. Closed-source models
• Strengths and limitations of each

2.4 Recent Trends & Important Concepts

• Multimodal AI (text + image + audio + video)
• Context windows and long-form understanding
• Retrieval-Augmented Generation (RAG)
• AI reasoning models (o1, o3)
• Real-time AI and streaming responses

2.5 Prompt Engineering Fundamentals

• What is a prompt?
• Anatomy of a good prompt: Role, Task, Context, Format, Constraints
• Text generation and completion
• Summarization: Short vs. detailed summaries
• Rewriting: Tone adjustment (formal, casual, persuasive, empathetic)
• Translation and localization

2.6 Prompt Structures

• Instructional prompts
• Role-based prompts (“Act as a…”)
• Template-based prompts
• Structured output requests (JSON, tables, lists)

2.7 Core Prompting Techniques

• Zero-shot prompting: Direct instructions
• Few-shot prompting: Learning from examples
• Chain of Thought (CoT): Step-by-step reasoning
• Tree of Thoughts (ToT): Exploring multiple reasoning paths
• Self-consistency: Multiple attempts for better answers

2.8 Multi-turn Interactions

• Context retention and conversation memory
• Building on previous responses
• Clarification and refinement loops

2.9 Limitations & Quality Evaluation

• • Hallucinations: What they are and how to spot them
  • Bias in AI outputs
  • Factual accuracy verification
  • Evaluating output quality: Relevance, coherence, accuracy, creativity
  • When to use AI vs. human judgment

3.1 LLM Deep Dive

• How transformers process language
• Attention mechanisms visualized
• Pre-training and next-token prediction
• Temperature and sampling parameters
• Top-p, Top-k sampling explained

3.2 LLMs vs. Small Language Models (SLMs)

• When to use LLMs vs. SLMs
• Edge deployment and efficiency
• Cost considerations

3.3 Popular LLM Comparison

• GPT-4: Strengths in reasoning and creativity
• Claude: Long context and nuanced understanding
• Gemini: Multimodal capabilities
• LLaMA: Open-source flexibility
• Use case mapping: Which model for which task?

3.4 Advanced Prompting Techniques

• Self-consistency prompting
• Instruction tuning and prompt optimization
• Negative prompting (what to avoid)
• Constraint-based prompting
• Persona switching and style guides
• Prompt chaining for complex tasks

3.5 Context Management

• Working with large documents
• Context window limitations and strategies
• Summarization for context compression
• Conversation history management

3.6 Introduction to Embeddings

• What are embeddings?
• Vector representations of text
• Semantic search applications
• Use cases: Document similarity, recommendation systems

3.7 Fine-tuning Basics

• What is fine-tuning?
• When to fine-tune vs. prompt engineer
• Transfer learning concept
• No-code fine-tuning platforms (brief overview)

4.1 Introduction to Agentic AI

• What is Agentic AI?
• Key characteristics: Autonomy, goal-directedness, adaptability
• Agentic AI vs. Traditional AI workflows
• Real-world examples: Research agents, customer service agents, coding agents

4.2 AI Agents vs. Chatbots

• Chatbots: Reactive, scripted interactions
• AI Agents: Proactive, goal-oriented, tool-using
• Comparison matrix

4.3 Components of Agentic AI

• Memory: Short-term and long-term context retention
• Planning: Breaking down goals into sub-tasks
• Tool Use: API calls, web search, code execution, database queries
• Autonomy: Self-directed task completion
• Reflection: Self-evaluation and improvement

4.4 Agent Frameworks Overview

• LangChain: Modular components for agent building
• AutoGen: Multi-agent conversations
• CrewAI: Role-based agent collaboration
• OpenAI Assistants API: Built-in agent capabilities
• Comparison and use case mapping

4.5 AI Orchestration

• What is orchestration?
• Workflow design for agent tasks
• Human-in-the-loop (HITL) integration
• Error handling and fallback strategies

5.1 No-Code AI Overview

• What is no-code/low-code?
• Benefits: Speed, accessibility, cost-effectiveness
• Limitations: Customization constraints, vendor lock-in
• When to use no-code vs. custom development

5.2 Workflow Automation Fundamentals

• Triggers, actions, and conditions
• API integrations basics
• Data mapping and transformation
• Error handling and testing

5.3 Zapier

• Platform overview and interface
• Building your first Zap
• Multi-step workflows
• AI-powered Zaps with ChatGPT integration
• Filters, formatters, and utilities
• Best practices and optimization
• Hands-on: Automate email-to-task workflow

5.4 Make.com

• Visual workflow builder
• Modules, routes, and scenarios
• Advanced routing and error handling
• Data stores and aggregators
• Scheduling and webhooks
• Hands-on: Build a content aggregation workflow

5.5 n8n

• Self-hosted automation (overview)
• Node-based workflow design
• Custom code nodes
• Integrations and credentials management
• Hands-on: Create a Slack notification system

5.6 Notion AI

• AI writing and editing
• Database automation
• Template creation with AI
• Q&A over workspace knowledge
• Hands-on: Build an AI-powered knowledge base

5.7 Airtable

• AI-powered data management
• Automation with AI fields
• Integration with other tools
• Building mini-apps
• Hands-on: Create a project tracker with AI summaries

5.8 Glide

• No-code app builder
• AI-powered features
• Data binding and workflows
• Mobile app prototyping
• Hands-on: Build a simple internal tool

5.9 Canva AI

• Text-to-design generation
• AI brand kit creation
• Magic Resize and Magic Eraser
• AI-powered content suggestions
• Hands-on: Create a presentation with AI

5.10 Tome AI

• AI-powered storytelling
• Auto-generating narratives
• Interactive presentations
• Hands-on: Build a pitch deck

5.11 Gamma.app

• AI slide deck creation
• One-click formatting
• Collaborative editing
• Hands-on: Generate a training module

5.12 Integrating AI into Business Processes

• Identifying automation opportunities
• Workflow mapping and optimization
• Change management considerations
• Measuring ROI of automation
• Building a toolkit for your role

6.1 Agent Design Principles

• Defining agent goals and scope
• Task decomposition
• User experience considerations
• Edge case handling

6.2 No-Code Agent Platforms

• Relevance AI: Building AI teams
• Stack AI: Workflow-based agents
• Flowise: Visual LLM app builder
• Voiceflow: Conversational agents
• Platform comparison and selection

6.3 Building Your First Agent

• Step-by-step agent creation
• Defining instructions and personality
• Adding tools and integrations
• Knowledge base integration (RAG)
• Testing and iteration

6.4 Practical: AI Research Assistant Agent

• Goal: Automated web research and summarization
• Components:
  1. Web search capability
  2. Content extraction
  3. Summarization
  4. Report generation
  5. Email delivery
  6. Tools: n8n/Make + GPT API + Google Docs
• Build process:
  1. Design the workflow
  2. Set up web search module
  3. Configure summarization
  4. Create output template
  5. Add scheduling
  6. Test and refine

6.5 Advanced Agent Features

• Memory and context persistence
• Multi-tool orchestration
• Conditional logic and branching
• User input handling
• Feedback loops

6.6 Deployment & Monitoring

• Publishing your agent
• Usage analytics
• Performance monitoring
• Iterative improvement
• Cost management

7.1 AI Challenges

Bias

• What is AI bias?
• Sources: Training data, algorithm design, human feedback
• Types: Gender, racial, cultural, socioeconomic
• Real-world examples and consequences
• Detection and mitigation strategies

Hallucinations

• What are hallucinations?
• Why LLMs hallucinate
• Identifying hallucinations
• Mitigation: Verification, grounding, confidence scoring

Privacy & Security

• Data privacy concerns
• Prompt injection attacks
• Data leakage risks
• Secure AI usage practices
• Compliance considerations (GDPR, CCPA)

Misinformation & Deepfakes

• AI-generated content detection
• Deepfake risks
• Watermarking and provenance
• Media literacy in AI age

7.2 Responsible AI Principles

• Fairness: Eliminating discriminatory outcomes
• Accountability: Clear ownership and responsibility
• Transparency: Explainability and disclosure
• Explainability: Understanding AI decisions
• Privacy: Data protection and consent
• Safety & Security: Robustness and resilience
• Human Control: Human-in-the-loop systems

7.3 Global AI Ethics Frameworks

OECD AI Principles

• Inclusive growth and sustainability
• Human-centered values
• Transparency and explainability
• Robustness and safety
• Accountability

EU AI Act

• Risk-based approach
• Prohibited AI practices
• High-risk AI systems
• Transparency obligations
• Compliance requirements

NITI Aayog (India)

• #AIForAll vision
• Responsible AI strategy
• Focus on explainability and fairness
• Sector-specific guidelines

UNESCO Recommendation on AI Ethics

• Human rights and dignity
• Environmental sustainability
• Cultural diversity
• Gender equality

Microsoft Responsible AI

• Six principles framework
• Impact assessments
• Governance structure
• Practical tools

7.4 Safe & Responsible Usage

Verifying AI Outputs

• Cross-referencing with trusted sources
• Fact-checking methodologies
• Using multiple AI models for comparison
• Critical evaluation frameworks

Human-in-the-Loop (HITL) Systems

• When to require human oversight
• Designing HITL workflows
• Balancing automation and control
• Decision authority frameworks

Trust & Compliance

• Building organizational AI policies
• Training and awareness programs
• Audit trails and documentation
• Incident response plans

7.5 Responsible AI Guardrails

• Input validation and filtering
• Output moderation
• Rate limiting and abuse prevention
• Content policies and guidelines
• Monitoring and alerting systems

7.6 Case Studies of AI Misuse

• Amazon’s biased recruiting tool
• COMPAS recidivism algorithm
• Deepfake political videos
• ChatGPT jailbreaks and prompt injection
• Lessons learned and best practices

7.7 Building Ethical AI Culture

• Organizational responsibilities
• Individual accountabilities
• Ethical decision-making frameworks
• Continuous learning and adaptation

Multimodal AI

• GPT-4 Vision, Gemini 1.5 Pro
• Audio and video generation
• Applications in business

AI Video & Audio Tools

• Runway ML, Synthesia, ElevenLabs
• Use cases: Training videos, marketing content
• Ethical considerations

Retrieval-Augmented Generation (RAG)

• What is RAG?
• Building knowledge bases
• No-code RAG solutions

AI in Code Generation

• GitHub Copilot, Cursor, Replit AI
• Low-code development acceleration
• When to use AI coding assistants