Prompt Engineering Techniques

This guide to prompt engineering techniques is completely based on the amazing hands-on repository by NirDiamant. All credit for the content and Jupyter notebooks goes to the original repository: Prompt Engineering.

I have added this here are it is an amazing learning resourse and full credits to Prompt Engineering Repo

🌱 Fundamental Concepts

1. Introduction to Prompt Engineering

#### Overview 🔎

A comprehensive introduction to the fundamental concepts of prompt engineering in the context of AI and language models.

#### Implementation 🛠️

Combines theoretical explanations with practical demonstrations, covering basic concepts, structured prompts, comparative analysis, and problem-solving applications.

1. Basic Prompt Structures

#### Overview 🔎

Explores two fundamental types of prompt structures: single-turn prompts and multi-turn prompts (conversations).

#### Implementation 🛠️

Uses OpenAI's GPT model and LangChain to demonstrate single-turn and multi-turn prompts, prompt templates, and conversation chains.

1. Prompt Templates and Variables

#### Overview 🔎

Introduces creating and using prompt templates with variables, focusing on Python and the Jinja2 templating engine.

#### Implementation 🛠️

Covers template creation, variable insertion, conditional content, list processing, and integration with the OpenAI API.

🔧 Core Techniques

1. Zero-Shot Prompting

#### Overview 🔎

Explores zero-shot prompting, allowing language models to perform tasks without specific examples or prior training.

#### Implementation 🛠️

Demonstrates direct task specification, role-based prompting, format specification, and multi-step reasoning using OpenAI and LangChain.

1. Few-Shot Learning and In-Context Learning

#### Overview 🔎

Covers Few-Shot Learning and In-Context Learning techniques using OpenAI's GPT models and the LangChain library.

#### Implementation 🛠️

Implements basic and advanced few-shot learning, in-context learning, and best practices for example selection and evaluation.

1. Chain of Thought (CoT) Prompting

#### Overview 🔎

Introduces Chain of Thought (CoT) prompting, encouraging AI models to break down complex problems into step-by-step reasoning processes.

#### Implementation 🛠️

Covers basic and advanced CoT techniques, applying them to various problem-solving scenarios and comparing results with standard prompts.

🔍 Advanced Strategies

1. Self-Consistency and Multiple Paths of Reasoning

#### Overview 🔎

Explores techniques for generating diverse reasoning paths and aggregating results to improve AI-generated answers.

#### Implementation 🛠️

Demonstrates designing diverse reasoning prompts, generating multiple responses, implementing aggregation methods, and applying self-consistency checks.

1. Constrained and Guided Generation

#### Overview 🔎

Focuses on techniques to set up constraints for model outputs and implement rule-based generation.

#### Implementation 🛠️

Uses LangChain's PromptTemplate for structured prompts, implements constraints, and explores rule-based generation techniques.

1. Role Prompting

#### Overview 🔎

Explores assigning specific roles to AI models and crafting effective role descriptions.

#### Implementation 🛠️

Demonstrates creating role-based prompts, assigning roles to AI models, and refining role descriptions for various scenarios.

🚀 Advanced Implementations

1. Task Decomposition in Prompts

   Overview 🔎

   Explores techniques for breaking down complex tasks and chaining subtasks in prompts.

   Implementation 🛠️

   Covers problem analysis, subtask definition, targeted prompt engineering, sequential execution, and result synthesis.

2. Prompt Chaining and Sequencing

   Overview 🔎

   Demonstrates how to connect multiple prompts and build logical flows for complex AI-driven tasks.

   Implementation 🛠️

   Explores basic prompt chaining, sequential prompting, dynamic prompt generation, and error handling within prompt chains.

3. Instruction Engineering

   Overview 🔎

   Focuses on crafting clear and effective instructions for language models, balancing specificity and generality.

   Implementation 🛠️

   Covers creating and refining instructions, experimenting with different structures, and implementing iterative improvement based on model responses.

🎨 Optimization and Refinement

1. Prompt Optimization Techniques

   Overview 🔎

   Explores advanced techniques for optimizing prompts, focusing on A/B testing and iterative refinement.

   Implementation 🛠️

   Demonstrates A/B testing of prompts, iterative refinement processes, and performance evaluation using relevant metrics.

2. Handling Ambiguity and Improving Clarity

   Overview 🔎

   Focuses on identifying and resolving ambiguous prompts and techniques for writing clearer prompts.

   Implementation 🛠️

   Covers analyzing ambiguous prompts, implementing strategies to resolve ambiguity, and exploring techniques for writing clearer prompts.

3. Prompt Length and Complexity Management

   Overview 🔎

   Explores techniques for managing prompt length and complexity when working with large language models.

   Implementation 🛠️

   Demonstrates techniques for balancing detail and conciseness, and strategies for handling long contexts including chunking, summarization, and iterative processing.

🛠️ Specialized Applications

1. Negative Prompting and Avoiding Undesired Outputs

   Overview 🔎

   Explores negative prompting and techniques for avoiding undesired outputs from large language models.

   Implementation 🛠️

   Covers basic negative examples, explicit exclusions, constraint implementation using LangChain, and methods for evaluating and refining negative prompts.

2. Prompt Formatting and Structure

   Overview 🔎

   Explores various prompt formats and structural elements, demonstrating their impact on AI model responses.

   Implementation 🛠️

   Demonstrates creating various prompt formats, incorporating structural elements, and comparing responses from different prompt structures.

3. Prompts for Specific Tasks

   Overview 🔎

   Explores the creation and use of prompts for specific tasks: text summarization, question-answering, code generation, and creative writing.

   Implementation 🛠️

   Covers designing task-specific prompt templates, implementing them using LangChain, executing with sample inputs, and analyzing outputs for each task type.

🌍 Advanced Applications

1. Multilingual and Cross-lingual Prompting

   Overview 🔎

   Explores techniques for designing prompts that work effectively across multiple languages and for language translation tasks.

   Implementation 🛠️

   Covers creating multilingual prompts, implementing language detection and adaptation, designing cross-lingual translation prompts, and handling various writing systems and scripts.

2. Ethical Considerations in Prompt Engineering

   Overview 🔎

   Explores the ethical dimensions of prompt engineering, focusing on avoiding biases and creating inclusive and fair prompts.

   Implementation 🛠️

   Covers identifying biases in prompts, implementing strategies to create inclusive prompts, and methods to evaluate and improve the ethical quality of AI outputs.

3. Prompt Security and Safety

   Overview 🔎

   Focuses on preventing prompt injections and implementing content filters in prompts for safe and secure AI applications.

   Implementation 🛠️

   Covers techniques for prompt injection prevention, content filtering implementation, and testing the effectiveness of security and safety measures.

4. Evaluating Prompt Effectiveness

   Overview 🔎

   Explores methods and techniques for evaluating the effectiveness of prompts in AI language models.