Professional Prompt Engineering with Generative AI-Syllabus
Module 1: Introduction to Artificial Intelligence
- Introduction to Artificial Intelligence
- AI vs Machine Learning vs Deep Learning vs Generative AI
- Evolution of Artificial Intelligence
- AI applications across industries
- AI benefits, limitations and ethics
- Future of AI careers
Module 2: Large Language Models (LLMs)
- What are Large Language Models?
- How LLMs work
- Tokens and tokenization
- Context window
- Temperature and AI creativity
- Hallucinations and AI limitations
- Popular LLMs (ChatGPT, Claude, Gemini)
Module 3: Prompt Engineering Foundations
- What is Prompt Engineering?
- Anatomy of an effective prompt
- Role, Context, Task, Constraints and Output Format
- Prompt writing principles
- Prompt lifecycle
- Delimiters and structured prompts
- Prompt frameworks (CLEAR, CREATE, PREP)
Module 4: Core Prompt Engineering Techniques
- Zero-shot Prompting
- One-shot Prompting
- Few-shot Prompting
- Role Prompting
- Chain-of-Thought Prompting
- ReAct Prompting
- Prompt refinement techniques
- Prompt evaluation and optimization
Module 5: Prompt Control & AI Behaviour Design
- Prompt conditioning
- Persona prompting
- Tone and style control
- Behaviour conditioning
- Output structure control
- System Prompt vs User Prompt
- Prompt hierarchy
Module 6: AI Productivity Tools
- ChatGPT for productivity
- Claude for documentation
- Gemini for research
- Perplexity AI
- NotebookLM
- Gamma AI
- Modern AI productivity workflows
Module 7: AI Applications & Industry Use Cases
- AI for communication
- AI for content creation
- AI for marketing
- AI for HR
- AI for customer support
- AI for logistics and manufacturing
- AI for presentations and reporting
Module 8: JSON, Structured Outputs & AI APIs
- Introduction to JSON
- JSON syntax
- Structured outputs
- AI API concepts
- Function calling fundamentals
Module 9: Python Essentials for Prompt Engineers
- Python installation
- Variables and data types
- Conditions and loops
- Functions
- Working with JSON
- Calling AI APIs
- Simple AI automation scripts
Module 10: AI Safety, RAG & AI Agents
- AI safety fundamentals
- Prompt injection and jailbreak awareness
- Hallucination handling
- Introduction to Retrieval-Augmented Generation (RAG)
- Introduction to AI Agents
- Context Engineering basics
- AI workflow automation
Module 11: Prompt Optimization & Career Readiness
- Prompt testing
- Prompt optimization
- AI quality evaluation
- Building a Prompt Portfolio
- LinkedIn profile optimization
- Resume building with AI
- AI interview preparation
- Career opportunities in Generative AI
Course Summary
- Total Duration: 60 Hours
- Projects: 2 Mini Projects & 1 Major Capstone Project
- Mini Projects: AI Prompt Library, AI Business Automation Assistant
- Major Project: AI Productivity Assistant / Enterprise AI Copilot
Tools Covered
- ChatGPT
- Claude
- Gemini
- Perplexity
- NotebookLM
- Gamma
- Modern AI Tools
Programming
- Python Basics
- JSON
- AI APIs
Career Roles
Prompt Engineer
Generative AI Associate
AI Content Specialist
AI Automation Associate
AI Productivity Consultant
AI Support Specialist
Junior AI Application Engineer
Prompt Designing with Generative AI for Working Professionals – Syllabus
Module 1: Introduction to Artificial Intelligence & Generative AI
- Introduction to Artificial Intelligence
- What is Generative AI?
- AI vs Traditional Software
- Popular AI tools (ChatGPT, Claude, Gemini, Copilot, Perplexity)
- Real-world workplace applications
- Benefits, limitations, and responsible AI usage
Module 2: Fundamentals of Prompt Designing
- What is a Prompt?
- Why Prompt Design Matters
- Anatomy of an Effective Prompt
- The RCTF Framework (Role, Context, Task, Format)
- Writing clear and specific prompts
- Common prompting mistakes
- Prompt refinement techniques
Module 3: Effective Prompting Techniques
- Zero-shot Prompting
- Few-shot Prompting
- Role-based Prompting
- Step-by-step prompting
- Output formatting
- Tone and style control
- Asking AI to think critically
- Iterative prompting for better results
Module 4: AI for Workplace Productivity
- Professional email writing
- Report generation
- Meeting minutes and summaries
- Presentation content creation
- Resume and LinkedIn enhancement
- Excel and data analysis assistance
- Brainstorming and decision support
- Daily productivity workflows
Module 5: AI Across Business Functions
- AI for Marketing & Content Creation
- AI for HR & Recruitment
- AI for Customer Support
- AI for Sales & Business Communication
- AI for Finance & Documentation
- AI for Operations & Logistics
- Industry-specific prompt examples
- Building a reusable Prompt Library
Module 6: Best Practices & Hands-on Workshop
- AI ethics and privacy
- Verifying AI-generated content
- Hallucinations and fact-checking
- Prompt improvement workshop
- Live business scenarios
- Q&A session
- Future learning roadmap
Course Summary
- Total Duration: 10 Hours
Practical Activities
- 25+ Hands-on Prompt Exercises
- Live AI Demonstrations
- Workplace Case Studies
- Prompt Improvement Challenges
- Personal Prompt Library Creation
AI Tools Covered
- ChatGPT
- Claude
- Gemini
- Microsoft Copilot
- Perplexity
- NotebookLM (Introduction)
Skills You'll Gain
- Design effective prompts for any AI tool
- Generate professional emails and reports
- Create presentations and business documents
- Summarize and analyze information quickly
- Improve workplace productivity using AI
- Build reusable prompt templates
- Use AI responsibly and confidently
Certification Awarded
- Certificate in Prompt Designing with Generative AI for Professionals
Learning Outcome
By the end of this 10-hour program, participants will be able to:
- Communicate effectively with Generative AI using structured prompts
- Save time by automating routine workplace tasks
- Create professional-quality content, reports, presentations, and emails
- Apply AI confidently in their daily roles across different business functions
- Build a personal prompt library for continued productivity
Target Audience
Working Professionals
Team Leaders
Managers
HR Professionals
Marketing Executives
Sales Professionals
Business Owners
Teachers & Trainers
Consultants
Administrative Professionals
Advanced Prompt Engineering with Generative AI – Syllabus
Module 1: Introduction to Artificial Intelligence
- Introduction to AI and Generative AI
- AI vs ML vs Deep Learning vs Gen AI
- Evolution of AI
- Real-world AI applications
- AI ethics and responsible usage
- AI benefits and limitations
Module 2: Large Language Models (LLMs)
- What is an LLM?
- Transformer architecture
- Tokens and tokenization
- Context window
- Parameters and scaling
- Temperature and model behaviour
- Hallucinations and limitations
Module 3: Prompt Engineering Foundations
- What is a prompt?
- Anatomy of an effective prompt
- Role, Context, Task, Constraints, Format
- Prompt design principles
- Prompt frameworks (CLEAR, CREATE, ERA, PREP)
- Prompt lifecycle management
- Delimiters and structured prompting
Module 4: Core Prompt Engineering Techniques
- Zero-shot prompting
- One-shot prompting
- Few-shot prompting
- Role-based prompting
- Chain-of-Thought prompting
- Self-Consistency prompting
- ReAct prompting
- Prompt calibration techniques
Module 5: Prompt Control & AI Behaviour Design
- Prompt control fundamentals
- Prompt conditioning
- Tone conditioning
- Persona conditioning
- Behavioural conditioning
- Output length and structure control
- System prompt vs user prompt
- Instruction priority handling
Module 6: AI Tools Mastery
- ChatGPT workflows
- Claude for research and documentation
- Gemini and Google AI ecosystem
- Perplexity for AI search
- NotebookLM for knowledge management
- Gamma for presentations
- AI productivity workflows
Module 7: AI Applications & Industry Use Cases
- AI for communication and documentation
- AI for marketing and branding
- AI for HR and customer support
- AI for manufacturing and logistics
- AI for presentations and reporting
- Industry AI project workshop
Module 8: JSON, Structured Outputs & APIs
- Introduction to JSON
- JSON structure and validation
- API fundamentals
- Working with AI APIs
- Structured outputs
- Function calling basics
Module 9: Python for AI Application Engineers
- Python installation and setup
- Variables and data types
- Strings and collections
- Conditions and loops
- Functions and modules
- Exception handling
- File handling
- Object-Oriented Programming
- JSON and API handling
- Prompt automation scripts
Module 10: Prompt Robustness, Safety & Guardrails
- AI safety fundamentals
- Hallucination handling
- Prompt injection attacks
- Jailbreak attempts
- Defensive prompting
- Guardrail design
- AI QA and validation
Module 11: RAG & Context Engineering
- Introduction to RAG
- Embeddings and vector databases
- Chunking strategies
- Knowledge base design
- PDF and website grounding
- Memory concepts
- Context engineering workflows
Module 12: AI Agents & Intelligent Workflows
- Introduction to Agentic AI
- Components of an AI agent
- Multi-agent systems
- Tool calling and orchestration
- Designing agent prompts
- Building intelligent workflows
Module 13: Enterprise AI Solution Design
- Requirement gathering
- Use case discovery
- Conversation design
- AI assistant design
- Workflow mapping
- Prompt documentation and libraries
Module 14: Prompt Optimization & AI Quality Assurance
- Prompt testing and refinement
- A/B testing
- Evaluation metrics
- Cost vs quality optimization
- Prompt versioning
- Production readiness testing
Module 15: Capstone Project & Career Readiness
- AI project planning
- Portfolio development
- Capstone project implementation
- LinkedIn optimization
- Resume enhancement using AI
- Interview preparation
- AI career opportunities
Course Summary
- Total Duration: 120 Hours
- Projects: 1 Major + 3 Mini Projects
- Tools Covered: ChatGPT, Claude, Gemini, Perplexity, NotebookLM, Gamma, Modern AI tools
- Programming: Python + APIs + JSON
Career Roles
Prompt Engineer
AI Application Engineer
Generative AI Associate
AI Agent Developer
AI Automation Specialist