Tracing is KEY if you want to understand and de...
You cannot debug an AI product without tracing. Every time a user sends a message to your AI app, a dozen things happen under the hood. A trace captures every single step, from the moment the first query hits your server, to a vector search which pulls context from your knowledge base with similarity scores for each piece of content it pulls, to the exact tokens your model generates, how long inference takes, and how much it costs. Without this, you have no idea why your product was wrong or gave a bad response. You could be collecting and examining hundreds of these traces every day. Companies are scrambling to hire people who can do this, so we brought in a former staff machine learning engineer at GitHub to teach you guys exactly how he debugs AI products. Find it at the link in my bio.
Summary
Tracing is crucial for debugging AI products as it captures every step in the process. Understanding this can help identify issues and improve AI responses. Companies are seeking skilled individuals in this area.
Key Points
- Tracing is essential for debugging AI products effectively.
- A trace captures every step from user query to model response.
- Understanding tracing helps identify why responses are incorrect.
- Companies need skilled professionals for AI product debugging.
- Learning tracing can enhance your AI engineering skills.
Tags
Repurpose Ideas
- LinkedIn post: Why tracing is vital for AI debugging.
- Tweet: Key steps in tracing for AI products.
- Checklist: Essential elements of AI product tracing.
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