Top Hat
  • About Top Hat
    • AI Agent Creation
    • HAT V2: The Biggest Upgrade to Top Hat Since Launch
    • $HAT Tokenomics
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    • Useful Links
  • Features
    • Prompt Engineering
    • Write a Good Prompt For An Agent
    • Safe, Neutral and NSFW Modes
    • Optional Tokenization and Verification
    • Credits System
    • Community Top-Ups
    • Telegram and Discord Interactions (Compulsory)
    • Autonomous Tweeting
    • TikTok Connections
    • Multi-agent Swarm
    • 3D Renders and IP
    • Image Recognition
    • Onchain Actions and Asset Management
    • Sandbox Testing Environment
  • ADVANCED
    • Knowledge Base and RAG Support
    • Webpage Uploads
    • API and Plug-in Store (Developers)
    • Function Calling (Developers)
    • Hooks and Multi-Agent Workflow (Developers)
  • Resources
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  1. Features

Prompt Engineering

Create and customize your agent with natural language.

PreviousUseful LinksNextWrite a Good Prompt For An Agent

Last updated 4 months ago

✍️ What is a Prompt?

Prompts are the instructions or context an AI agent uses to generate responses. They set the agent’s personality, guide its tasks, and outline what it can or cannot do. By crafting the right prompts, agent creators can shape the agent’s behavior and keep the agents focused on prompted goals. This flexibility allows agent creators to adjust the agent’s role, style, or purpose without modifying the underlying AI model.

The prompt serves as the foundational blueprint of the agent, effectively setting his core attributes and behaviors. Any significant changes to the agent’s personality or behavior should be made directly within the prompt. This ensures that fundamental adjustments are consistently applied.

Is Fine-Tuning Necessary?

Most likely no. While fine-tuning (retraining a large model like OpenAI’s GPT or Anthropic’s Claude on specialized data) can improve performance in specific scenarios, it’s often redundant and cost-inefficient because these models are already quite capable out of the box. You can usually achieve the same results by guiding the model with targeted prompts, avoiding extra training costs. Plus, as providers frequently update their base models, fine-tuned versions become outdated faster, making prompt-based solutions both more affordable and more adaptable.