The Prompt Engineering Course is designed to equip learners with the skills to interact effectively with AI models like ChatGPT, GPT-4, and other large language models (LLMs). This course is ideal for AI enthusiasts, developers, content creators, product managers, and anyone seeking to harness the power of generative AI through strategic prompt crafting. Mastering prompt engineering helps you generate accurate, creative, and context-aware outputs, making AI tools significantly more productive in real-world applications.

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Instructor led live online Classes

Prompt Engineering Course

Instructor led live online Training (Weekday/Weekend)

Why Enroll in the Prompt Engineering Course?

Maximize AI Output: Learn to write powerful prompts that guide AI models to deliver useful results.
Enhance Productivity: Use prompts to streamline coding, content creation, research, and automation.
Career Opportunities: Prompt engineering is a high-demand skill in AI, content, and product roles.
Real-World Practice: Work on projects involving chatbots, creative writing, summarization, and more.
Stay Ahead of the Curve: Be an early adopter of emerging AI communication skills.

Course Description

This course teaches the principles, techniques, and frameworks needed to craft effective prompts for AI models, ensuring better task performance, clarity, and control over generated outputs.

1. Developers and Engineers integrating AI into applications. 2. Content Creators and Marketers using AI for writing and ideation. 3. Students and Educators interested in exploring AI tools. 4. Business Professionals automating workflows and insights.

1. Teaches prompt patterns and optimization techniques. 2. Includes real-time demonstrations and prompt debugging. 3. Provides frameworks for designing multi-step AI workflows.

What you'll learn

Understanding LLM Behavior: How large language models interpret and respond to input.

Prompt Structures and Patterns: Zero-shot, one-shot, few-shot, chain-of-thought, and role-based prompting.

Prompt Tuning and Refinement: Techniques for improving response quality.

Use Cases Across Domains: Code generation, content creation, business analysis, and education.

Multi-Model Interactions: Integrating prompts with tools like ChatGPT, Claude, Bard, and open-source models.

AI Safety and Ethics: Responsible usage, bias mitigation, and prompt testing.

Requirements

Basic familiarity with AI tools like ChatGPT or similar models.
No prior programming experience required, but helpful for advanced modules.

Curriculum Designed by Experts

Generative AI Principles
Types of Generative Models
Applications of Generative Models
Machine Learning Algorithms with GenAI
Applications of Generative AI
Generative AI: Advantages and Disadvantages
Ethical Considerations

Implementing Generative AI Use Cases
Implementing Machine Learning with GenAI

Natural Language Processing (NLP) Essentials
Text Classification
Text Preprocessing
Basic NLP Tasks
Deep Learning for NLP
Neural Networks
Backpropagation
RNN
LSTM
Deep Learning Applications in NLP

Simple Text Classification Task
Implementing Core NLP Tasks
Working with Deep Learning for NLP

Basic Autoencoders
Variational Autoencoders (VAEs)
Applications in Data Compression and Generation
Basic GAN Architecture
Training GANs
Variants of GANs
DCGAN
CycleGAN

Working with GANs
Data Compression with Autoencoders
Training Variants of GANs

Exploring Language Models
Types of Language Models
Applications of Language Models
The Transformer Architecture: Attention Mechanism
Advanced Transformer Models
GPT
BERT
Applications of Transformer-based Models

Working with GPT
Implementing BERT
Applying Advanced Transformer Models

Prompt Engineering Principles
What is Prompt Engineering?
Importance and Applications
Prompt Design Strategies
Types of Prompting
Crafting Effective Prompts
Parameter Tuning

Designing Precise Prompts
Experimenting with Various Prompt Design Strategies
Advanced Parameter Tuning for Prompt Engineering

LLMs and Generative AI Project Lifecycle
LLM Pre-Training and Scaling
Fine-Tuning LLMs with Specific Instructions
Efficient Fine-Tuning of Parameters
Reinforcement Learning from Human Response

Experimenting with LLM
Applying Fine-Tuning on Parameters
LLM Project Lifecycle
Reinforcement Learning Exercises

Search Query Completion
Next Word Prediction
Word Embeddings
Transformers
Generating Text
Stacking Attention Layers

Execute Search Query and Next-word Prediction using LLMs
Implement LLMs with TensorFlow and PyTorch
Build Chatbot using LLMs

LangChain Foundations
Benefits of using LangChain
Using LangChain to Develop LLM Applications
Value Propositions of LangChain
Components of LangChain
Off-the-Shelf Chains in LangChain
Build and Deploy LLM-Powered Applications using LangChain

Build an LLM-Powered Application using LangChain
Experiment with Off-the-Shelf Chains in LangChain
Deploy an LLM Application

Understanding Retrieval-Augmented Generation (RAG)
Document Loading and Splitting
Vector Stores and Embeddings
Retrieval
Question Answering with Chatbots
Building RAG Models using LangChain

Construct a LangChain-RAG Chatbot for Custom Data
Deploy Chatbot in a Production Environment
Develop a Basic RAG Model

Cloud Computing Foundations
AWS S3
Amazon EC2 Trn1n
Amazon EC2 Inf2
Amazon Sagemaker
Amazon CodeWhisperer
Amazon Bedrock
Azure OpenAI

Setting up a Cloud Computing Environment
Navigating Cloud Platforms for AI Development
Utilizing Amazon Elastic Compute Cloud (EC2) Trn1n Instances for AI Workloads
Building and Deploying Machine Learning Models using SageMaker

Introduction to ChatGPT
Leveraging ChatGPT for Productivity
Mastering Excel through ChatGPT
Becoming a Data Scientist using ChatGPT
Data Analysis in PowerBI with ChatGPT
Creating a Content Marketing Plan
Social Media Marketing using ChatGPT
Keyword Search and SEO using ChatGPT
Generating Content using ChatGPT
Implementing ChatGPT for Customer Service
ChatGPT for Developers
ChatGPT for Creating Programs
ChatGPT for Debugging
ChatGPT for Integrating New Features
ChatGPT for Testing
Documenting the Code with ChatGPT

Explore ChatGPT's Capabilities
ChatGPT for Programming and Productivity Enhancement
Leveraging ChatGPT for Debugging and Integrating New Features
Experiment with ChatGPT for Testing Purposes and Generating Test Cases
Practice Documenting Code and API Concepts with ChatGPT

Python Code Generation with Generative AI
Gen AI Tools for Coding
Advanced Code Optimization with ChatGPT Gen AI Tool
Coding with ChatGPT
Building an Application in Python with ChatGPT

Utilize Gen AI Tools to Enhance Coding Tasks
Optimize a Code using the ChatGPT Gen AI Tool
Create Python Scripts using ChatGPT

LLM Performance Comparison
Perplexity
BLEU Score
Human Evaluation
Choosing the Right Metrics
Interpreting the Results

Evaluate LLM using Various Performance Metrics
Select Appropriate Metrics for Specific Generative AI Task
Analyze LLM Performance

In-class Project: AI-Powered Text and Image Generator
Case Study: Generative AI for Personalized Learning
Case Study: Generative AI for Creative Content Generation
Case Study: Generative AI for Business

Building a Generative AI Powered Text and Image Generator

Artificial Intelligence Essentials
Disciplines of AI
Types of AI
Machine Learning Fundamentals
Predictive ML Models
ML Algorithms: Deep Dive
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Reinforcement Learning

Experiment with Various AI Tools
Implement a ML Model
Explore Different Disciplines and Types of AI

Hugging Face Transformers
OpenAI GPT3 API
Google Cloud AI Platform
MidJourney
DALL E-2
Google Bard

Implement a Basic Model using Hugging Face Transformers
Generate content with OpenAI GPT3 API
Deploy a Model on Google Cloud AI Platform
Generate Images using MidJourney and DALL E-2
Working with Google Bard

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Prompt Engineering Course

Read Review

Instructor led live online Classes

Prompt Engineering Course