Large Language Models
Prompt engineering is the art and science of crafting effective instructions for language models. Well-designed prompts can dramatically improve output quality, accuracy, and relevance.
Key Techniques
- Clear Instructions: Explicit, specific directions
- Few-Shot Examples: Providing examples of desired behavior
- Chain-of-Thought: Asking the model to explain its reasoning
- Role Assignment: Defining the model’s persona or expertise
- Format Specification: Requesting specific output structures
Best Practices
- Be specific and detailed
- Provide context and constraints
- Use delimiters to separate sections
- Iterate and refine based on results
- Test edge cases
Prompt engineering has become a valuable skill in AI product development and research.
Tags
llm technique practical
Related Terms
Chain-of-Thought
A prompting technique where the model explains its reasoning step-by-step before giving a final answer, improving complex reasoning.
Few-Shot Learning
Learning to perform a task from a small number of examples provided in the prompt, without parameter updates.
In-Context Learning
The ability of LLMs to learn from examples and instructions provided in the input prompt without training.
System Prompt
Initial instructions defining the model's role, behavior, and constraints for the conversation.