Prompt Engineering
Also known as: Prompting
Crafting the input text so the model produces the behavior you want.
The practice of designing input prompts to elicit desired behavior from LLMs. Techniques include few-shot examples, chain-of-thought reasoning, system messages, and structured output formatting.
In practice
Often the first lever pulled in production. Interviews probe few-shot vs zero-shot, chain-of-thought, and when to graduate to fine-tuning or RAG.
How it compares
Prompt engineering changes the input; fine-tuning changes the model.
Comparisons that include Prompt Engineering
Related topics
Related terms
AI Agents
LLMs that loop: plan → call tools → observe results → repeat until done.
Model Context Protocol (MCP)
An open protocol (from Anthropic) for letting LLMs call tools and read resources from any compatible server.
Chain-of-Thought (CoT)
Ask the model to think step by step before answering. It boosts accuracy on reasoning tasks.
In-Context Learning (ICL)
Show the model a few examples in the prompt and it learns the pattern, no fine-tuning needed.
Function Calling
The model emits structured JSON specifying a tool call instead of free text; the host executes it.
Prompt Injection
Attacker text in user input or retrieved content overrides the model's system prompt or hijacks tool calls.