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AI is the cornerstone of Clay's functionality, surpassing traditional integrations in both utility and impact. This guide outlines essential best practices for AI prompting in Clay, drawing from extensive experience and common pitfalls observed in real-world applications. Understanding these principles is crucial for maximizing AI's potential in data enrichment and content management tasks.

Whether you're working on data collection, analysis, or content generation, mastering these AI best practices will significantly enhance your efficiency and accuracy in Clay. The following sections detail key strategies, from maintaining single-task focus to optimizing costs, ensuring you get the most value from AI-powered workflows.

Top 10 AI Best Practices in Clay

1. Single-Task Focus

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Each AI prompt should focus on one specific task to maintain clarity and reduce errors. For example:

• ✅ DO: Separate data collection and analysis into distinct steps
  • First prompt: Scrape website/page for information
  • Second prompt: Analyze collected information for industry classification
• ❌ DON'T: Combine multiple tasks like scraping multiple sources and making classifications in one prompt

2. Context Management

Be strategic with the context provided to AI:

• ✅ DO: Include only relevant, specific properties needed for the task
• ❌ DON'T: Add unnecessary data arrays or redundant information that could confuse the AI or increase costs

3. Prompt Clarity & Detail

Prompts should be detailed and well-structured. Compare these examples:

• Bad Industry Segmentation Prompt Example
• Vague instructions
• No category descriptions
• Limited guidance on classification