Prompt Engineering
The practice of designing and optimizing input prompts to get the best possible output from AI models. It involves crafting instructions, providing examples, and structuring queries to guide the model toward desired responses.
Why It Matters
Effective prompt engineering can dramatically improve AI output quality without any model changes — it is the most accessible way to get better results from LLMs.
Example
Adding 'Think step by step' to a math problem prompt, or providing three examples of the desired output format before asking the model to generate its own.
Think of it like...
Like being a great interviewer — the quality of answers you get depends heavily on how well you frame your questions, provide context, and guide the conversation.
Related Terms
Few-Shot Learning
A technique where a model learns to perform a task from only a few examples provided in the prompt. Instead of training on thousands of examples, the model generalizes from just 2-5 demonstrations.
Zero-Shot Learning
A model's ability to perform a task it was never explicitly trained on or shown examples of. The model applies its general knowledge and reasoning to handle entirely new task types.
Chain-of-Thought
A prompting technique where the model is encouraged to show its step-by-step reasoning process before arriving at a final answer. This improves accuracy on complex reasoning tasks.
System Prompt
Hidden instructions provided to an LLM that define its behavior, personality, constraints, and capabilities for a conversation. System prompts set the rules of engagement before the user interacts.
Prompt Injection
A security vulnerability where malicious input is crafted to override or manipulate an LLM's system prompt or instructions, causing it to behave in unintended ways.