Gen AI and Prompt Engineering

Content

Modules Included:

Gen AI:

Unit -1: Introduction to Generative AI

• Overview of Generative AI
• Distinction between traditional AI and Generative AI
• Applications of Generative AI in image, text, and audio generation

Unit-II: Fundamentals of Neural Networks

• Definition and overview of neural networks
• Biological inspiration for artificial neural networks
• Basic structure of a neural network: neurons, layers, and connections
• Activation functions: sigmoid, tanh, ReLU
• Forward Propagation and Backward Propagation

Unit-III: Generative Models

• Understanding GANs in-depth
• Exploring GAN architectures (DCGAN, WGAN, etc.)
• Training strategies for stable GANs
• Applications of GANs in image generation, style transfer, and data augmentation
• Lab: Implementing a GAN for Audio generation

Unit IV- Variational Autoencoders (VAEs) and LSTM

• Variational Autoencoders (VAEs) deep dive
• Long Short-Term Memory (LSTM) networks

Unit V: Architecture Large Language Models (LLM)

• Overview of popular LLMs in the industry (e.g., GPT-3, BERT, T5).
• Applications in various domains
• Transformer Architecture

Unit VI: Different Open-Source Large Language Models

• Overview of Hugging Face Transformers
• Loading and using pre-trained models from Hugging Face

Unit VII: ChatGPT-4

• Overview of GPT-4 architecture and capabilities
• Use cases and applications.

Unit VIII: Text classification, Language Generation and Summarization

• LLMs in Text Classification
• Implementing LLMs for Text Classification
• Advantages of Using LLMs for Text Classification
• Challenges of Using LLMs for Text Classification
• Generating creative text using LLMs
• Example: Generating creative text using LLMs
• LLM to generate more coherent text

Unit IX: Retrieval-Augmented Generative models (RAGs)

• Understanding the concept of retrieval-augmented models
• Applications of RAGs in NLP tasks
• Implementing a simple RAG model for information retrieval
• Lab: Building a basic RAG model

Unit X: Image Generation, Diffusion Model, DALL·E, Bing

• Understanding Diffusion Models
• Applications of Diffusion Model
• DALL-E
• Bing’s Image Generation Techniques
• Bing’s Image Generation-Application
• Lab: Implementing an image generation model using diffusion

Prompt Engineering

Unit - I: Introduction to AI & Language Models

• Artificial Intelligence
• Basics of Machine Learning
• Introduction to Natural Language Processing
• Key Challenges in NLP
• Overview of Language Models
• Understanding Transformers 
• How Language Models Learn
• Applications of Language Models in the Real World
• Setting Up a Python Environment for NLP (LAB)

Unit - II: Foundation of Prompt Engineering

• What is Prompt Engineering
• Importance of Prompt Design in AI Interactions
• Types of NLP Learning in Prompting

a.) Zero Shot Learning - Theory, Examples and Analysis (LAB)

b.) One Shot Learning - Theory, Examples and Analysis (LAB)

c.) Few Shot Learning – Theory, Examples and Analysis (LAB)

• Understanding Model Responses to Prompts
• Best Practices in Prompt Construction
• LAB: - Hands-on: Crafting Effective Prompts for Different Scenarios

Unit - III: Prompting Techniques

1. Chain of Thoughts Prompting
   • • Fundamentals
     • Role
     • Effective Strategies
     • Benefits and Limitations
     • Practical Applications of Chain-of-Thought Prompting
     • Case Studies
     • LAB: -Hands-on: Developing a Chain of Thoughts Prompting System
     • Overcoming Challenges

1. Iterative Prompting
   • • Fundamentals
     • Role
     • Effective Strategies
     • Benefits and Limitations
     • Practical Applications of Iterative Prompting
     • Case Studies
     • LAB: -Hands-on: Developing an Iterative Prompting System
     • Overcoming Challenges

1. Negative Prompting
   • • Fundamentals
     • Role
     • Effective Strategies
     • Benefits and Limitations
     • Practical Applications of Negative Prompting
     • Case Studies
     • LAB: -Hands-on: Developing a Negative Prompting System
     • Overcoming Challenges

1. Hybrid Prompting
   • • Fundamentals
     • Role
     • Effective Strategies
     • Benefits and Limitations
     • Practical Applications of Hybrid Prompting
     • Case Studies
     • LAB: -Hands-on: Developing a Hybrid Prompting System
     • Overcoming Challenges

Unit - IV: Prompt Chaining

• The Concept of Prompt Chaining and Its Applications
• Designing Effective Prompt Chains for Sequential Tasks
• Understanding the Logic Flow in Prompt Chains
• Case Examples: Prompt Chaining for Data Processing
• Analysing Results and Feedback Loops in Prompt Chaining
• Building Your Own Prompt Chain: A Step-by-Step Guide
• LAB: - Hands-on: Implementing a Prompt Chaining System for a Chosen Domain
• Advanced Prompt Chaining Techniques
• LAB: - Troubleshooting and Refining Prompt Chains
• Future Directions in Prompt Chaining Technology

Unit - V: Advanced Prompting Techniques

• • Overview of Advanced Prompting Techniques
  • Meta-Prompting: Theory and Applications
  • Prompt Tuning and Fine-Tuning Language Models
  • Leveraging External Knowledge Bases in Prompting
  • Advanced Evaluation Metrics for Prompting Strategies
  • Practical Implementations of Advanced Techniques
  • LAB: -Hands-on: Meta-Prompting for Custom Use Cases
  • LAB: -Developing a Fine-Tuned Model for a Specific Task
  • Challenges and Solutions in Advanced Prompting

Unit - VI: Ethical Considerations in Prompt Engineering

• The Ethical Landscape of Prompt Engineering
• Bias, Fairness, and Transparency in AI Responses
• Privacy Concerns with Language Models
• Ethical Design and Implementation of Prompts
• Global Perspectives on AI Ethics
• Developing Ethical Guidelines for Prompt Engineering
• LAB: - Hands-on: Analysing and Mitigating Bias in AI Outputs
• Case Studies: Ethical Prompt Engineering in Practice
• Building Trustworthy AI Systems through Responsible Prompting

What does a Gen AI and Prompt Engineer do?

A Prompt engineer possesses a diverse set of skills and knowledge required to perform various tasks and responsibilities related to prompt engineering. With exposure to different GPT's, they are adept at designing effective prompts, optimizing model performance, troubleshooting, debugging, and more. Their expertise plays a crucial role in driving innovation, creating engaging conversational experiences, and optimizing the performance of language models. As a sought-after professional in the industry, a multi-platform prompt engineer leverages their expertise to deliver cutting-edge solutions with precision, speed, and efficiency.

What are the domains where Gen AI and Prompt Engineer work?

A prompt engineer works across various domains and industries that leverage natural language processing and AI technologies. Some of the common domains where a multi-platform prompt engineer may work include.

• • Chatbot Development
  • Virtual Assistants
  • Customer Support
  • Content Generation
  • AI Research
  • Language Model Fine-Tuning
  • Social Network, Media, and Communication
  • SaaS industry
  • E-Commerce and Finance

Top Job Roles of Gen AI and Prompt Engineer

• • AI Prompt Engineer
  • Conversational AI Developer
  • NLP Engineer
  • AI Solution Architect
  • AI Research Scientist
  • ChatGPT/Bard/Bing Engineer

Salary of a Gen AI and Prompt Engineer in India.

According to recent surveys and reports, the average annual pay package for a Gen AI and prompt engineer in India is around ₹16 lakhs per year. However, the salary may vary depending on factors such as years of experience, skills, location, and the organization you work for. An Entry level Gen AI and Prompt Engineer gets an offer of ₹5.6 lakhs per year. Senior professionals with a considerable amount of experience can earn up to ₹30 lakhs per annum. Some startups and large technology firms offer higher salaries to attract top talent for critical roles in conversational AI and NLP technologies.

Is it worth to pursue a career in Gen AI and Prompt Engineer?

It's worth noting that the field of Gen AI and Prompt Engineering is rapidly evolving, and demand for skilled engineers is expected to increase in the coming years. With this projected growth, salaries may continue to rise, making it a lucrative career choice.