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Prompt Engineering

AI / MCP

Definition

Prompt engineering is the practice of designing and refining instructions given to language models to achieve desired outputs. It involves crafting system prompts, few-shot examples, and structured queries.

How It Relates to CrawlForge

Good prompt engineering determines whether an AI agent uses tools effectively. A well-designed prompt tells the agent when to use CrawlForge tools, which tool to choose for each task, and how to interpret the results.

When using CrawlForge with AI agents, prompt engineering helps optimize credit usage. For example, instructing an agent to use extract_text (1 credit) for simple content retrieval instead of deep_research (10 credits) when full research is unnecessary can significantly reduce costs.

Related CrawlForge Tools

deep_research
10 credits
extract_text
1 credit

Related Terms

Large Language Model (LLM)

A large language model is a neural network trained on vast amounts of text data that can understand and generate human language. LLMs power AI assistants, code generators, and autonomous agents.

AI Agent

An AI agent is an autonomous system powered by a large language model that can reason about tasks, make decisions, and take actions by using tools. Agents go beyond simple chatbots by planning and executing multi-step workflows.

Context Window

The context window is the maximum amount of text (measured in tokens) that a language model can process in a single request. It includes both the input prompt and the generated output.

Token

A token is the basic unit of text that language models process. Text is split into tokens (roughly 4 characters or 0.75 words each) before being processed by the model. Token counts determine costs and context limits.

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