Prompt Engineering Masterclass

Advanced prompting techniques for LLMs with working GPT-4 code examples.

Techniques Covered

Technique	Description	Use Case
Few-Shot Learning	Provide examples to guide output format	Classification, formatting
Chain-of-Thought	Step-by-step reasoning	Math, logic, complex problems
Persona Prompting	Define LLM personality/expertise	Domain-specific tasks
Self-Consistency	Multiple reasoning paths, majority vote	Improved accuracy
Iterative Refinement	Self-reflection and improvement loops	Quality enhancement
Role-Playing	Scenario-based dialogue generation	Creative writing, simulation

What You'll Learn

• How to shape LLM behavior through systematic prompt design
• Techniques to improve output accuracy and consistency
• Methods for guiding code generation with explanations
• Strategies for maintaining context in multi-turn conversations

Notebooks

Each notebook includes working code examples using the OpenAI API:

1. Persona-based Prompting - Shape the LLM's "personality" for specific domains
2. Few-Shot Learning - Demonstrate desired input-output format with examples
3. Code-based Prompting - Guide code generation with explanations
4. Chain-of-Thought - Encourage logical step-by-step reasoning
5. Iterative Refinement - Improve responses through self-reflection
6. Role-Playing Scenarios - Create engaging narratives and dialogues

Quick Start

pip install openai
export OPENAI_API_KEY=your_key_here
jupyter notebook

Requirements

• Python 3.8+
• OpenAI API key
• Jupyter Notebook

Tech Stack

• LLM: GPT-4 / GPT-4-Turbo
• API: OpenAI Python SDK
• Environment: Jupyter Notebooks

License

MIT