Prompt Engineering is the practice of designing and optimizing input prompts to get the most effective and accurate responses from AI models, especially Large Language Models (LLMs) like GPT-4, Claude, and Gemini. It is a crucial skill for improving AI-generated content, enhancing automation, and ensuring high-quality outputs.
Week-1. Introduction to Prompt Engineering
What is Prompt Engineering?, Role in AI, Use Cases
Week-2. Understanding LLMs & Tokenization
Transformer Models, Token Limits, Stop Words
Week-3. Basic Prompting Techniques
Zero-Shot, Few-Shot, One-Shot Learning
Week-4. Advanced Prompting Techniques
Chain-of-Thought (CoT), Self-Consistency, RAG
Week-5. System & Role-Based Prompts
Instruction-Tuned Models, Context Optimization
Week-6. Multi-Turn & Conversational Prompts
Maintaining Context, Memory-Augmented Models
Week-7. Programmatic Prompting & Templates
Dynamic Inputs, API-Based Prompting
Week-8. Optimizing Prompts for Different LLMs
OpenAI, Anthropic, Mistral, LLaMA, Falcon
Week-9.When to Fine-Tune vs Use Prompts
LoRA, PEFT, RLHF, Custom Model Training
Week-10. Building AI Applications with Prompts
LangChain, AutoGPT, AI Agents
Week-11. Debugging & Evaluating Prompts
Prompt Performance Metrics, Bias Detection
Week-12. Capstone Project – Part 1
Defining the AI Task, Crafting Prompts
Week-13. Capstone Project – Part 2
Iterating, Deploying, and Optimizing