This collection covers comprehensive prompt engineering techniques, arranged from foundational concepts to advanced strategies for working effectively with Large Language Models (LLMs).
Explores different types of PromptTemplate structures including StringPromptTemplate, ChatPromptTemplate, and MessagePlaceholder. Covers multiple ways to create and use templates programmatically with LangChain for consistent prompt formatting.
Introduces variable substitution in prompts using different syntax patterns. Covers single braces {variable} for direct substitution and double braces {{ variable }} for template engines. Essential for creating dynamic and reusable prompts.
Focuses on designing clear and specific directives for LLMs to guide behavior and output. Emphasizes the how-to approach, ensuring models understand tasks, constraints, and desired formats for more predictable and higher-quality results.
Introduces zero-shot prompting where LLMs handle tasks without relying on prior examples or task-specific training. Covers direct task specification and role-based prompting techniques. Best for straightforward tasks where examples aren't necessary.
Demonstrates few-shot (multi-shot) prompting by providing models with a few task-and-response examples before final requests. Shows how examples serve as guides for model behavior and output format, improving performance and consistency over zero-shot approaches.
Explores various prompt formats including Q&A, dialogue, and instructions. Covers structural elements like headings, bullet points, and numbered lists. Demonstrates how different formats and structures significantly impact AI model response quality and consistency.
Demonstrates how Chain of Thought (CoT) prompting works by encouraging models to show their reasoning steps. Improves accuracy on complex reasoning tasks by breaking down problem-solving into intermediate steps.
Explores negative prompting strategies and techniques for preventing undesired outputs from LLMs. Shows how to specify what the model should NOT do alongside positive instructions for better output control.
Covers creation and application of specialized prompts for diverse tasks including text summarization, question-answering, code generation, and creative writing. Provides task-specific best practices and examples.
10. Advanced Cases
Demonstrates advanced prompt engineering including constraints, rule-based prompts, and regex usage. Includes comparative analysis showing how different roles in prompts affect output quality for the same task.
Introduces task decomposition methodology for breaking complex problems into manageable subtasks. Emphasizes structured problem-solving approaches that improve model performance, enhance reasoning transparency, and make outputs more reliable and interpretable.
Addresses the balance between providing sufficient context for task understanding and maintaining prompt conciseness for efficiency. Provides strategies for managing prompt complexity while optimizing performance and token usage.
Discusses ethical considerations in prompt engineering, focusing on avoiding bias and ensuring fairness and inclusivity in prompts. Includes practical examples and references for responsible AI use.
Provides strategies for managing prompt length and complexity, balancing detail with conciseness, and handling long contexts for efficient LLM interactions.
A YAML-based prompt template for querying information about a country's capital city and its leader. Demonstrates structured prompt formatting and variable substitution for reusable, consistent prompt design.