You need to incorporate domain knowledge into your AI models. What methods will you choose?
To effectively incorporate domain knowledge into AI models, it’s essential to use methods that enhance accuracy and applicability. Here are some practical strategies:
What methods have you used to integrate domain knowledge into your AI models? Share your experience.
You need to incorporate domain knowledge into your AI models. What methods will you choose?
To effectively incorporate domain knowledge into AI models, it’s essential to use methods that enhance accuracy and applicability. Here are some practical strategies:
What methods have you used to integrate domain knowledge into your AI models? Share your experience.
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This is my favorite! Because it’s truly the foundation of AI—having a solid knowledge base is essential, and it relies more on the business side than on the AI expert. The AI expert will ask for countless examples and always remind you: What AI gives you is based on what you provide. That’s why it’s crucial to sit down with end users and deeply understand the problem. Define the functional requirements of what they need and identify the information that’s truly relevant. It’s far better to understand how the documentation is structured and determine the ground truth before feeding the AI thousands of unprocessed examples. Work with the business team is essential to define requirements, identify relevant data, and fine-tune AI for success
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I've discovered that successful domain knowledge integration hinges on strategic collaboration with subject matter experts. Through implementing numerous projects, I've observed that feature engineering based on expert insights consistently outperforms purely data-driven approaches. For example, when developing a customer service AI model, I found that incorporating service representatives' workflow patterns as features improved response accuracy by 30%. The key was systematically documenting their decision-making process.