Addressing Drift in Customer Claygent Builder Prompts

Sydney Y. · 2025-12-15T20:37:30.381Z

Why is my customer Claygent Builder prompt drifting? Even with the most concrete examples and strict instructions, the confidence has been regressing over time and the information is no longer accurate. Do I need to constantly switch between different models?

Addressing Drift in Customer Claygent Builder Prompts | Clay

8 comments

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Clay Support
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Prompt drift is common and fixable without constantly switching models. The key is systematic prompt refinement. [1]
Identify patterns: If the same failure type appears 5+ times, it's a prompt issue, not data quality. Add specific instructions to handle these failure patterns, then reprocess failed records with your updated prompt.
Use Claygent Builder's advantages: Since it's centralized at workspace level with version control, you can test prompt changes for free before deploying. [2] [3] This prevents the trial-and-error costs you'd face with individual table prompts.
Check your model settings: Recent Claude Sonnet 3.7 updates use more conservative token estimation, which actually improves stability. The issue likely isn't the model itself but prompt specificity.
Focus on iterating your prompt logic rather than switching models - that's usually the most effective fix.
Clay Support
APP
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Is that what you were looking for?
Sydney Y.
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I switched from argon to chat 5 and that seemed to greatly increase accuracy. Would you say that it is still a prompt issue?
Clay Support
APP
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Switching models can definitely improve accuracy - GPT-5 shows 8-17% better research accuracy compared to previous models. [1] However, even with a better model, prompt refinement is still valuable. The Claude Sonnet 3.7 updates use more conservative token estimation which actually improves stability, so the core issue is usually prompt specificity rather than the model itself.
Your accuracy boost from switching suggests the model change was the right move, but you can likely get even better results by combining that stronger model with refined prompts based on any remaining failure patterns you see.
Clay Support
APP
·
·
Did that answer your question?
Sydney Y.
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Yes but even using the AI refining tool, the agent was still regressing
Clay Support
APP
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·
Even with AI refining tools, regression can happen due to systematic issues. Try this approach: identify if the same failure pattern appears 5+ times - that indicates a prompt structure problem rather than individual cases. [1]
The Claude Sonnet 3.7 updates use more conservative token estimation which actually improves stability, so the regression might be from inconsistent prompt logic rather than the model itself.
Start with small test batches of 10-20 rows to validate your refined prompts before scaling up. If the AI formula still isn't producing right output, use the red "Output is Wrong" button to iterate on prompts and refine logic. [2]
What specific failure patterns are you seeing repeatedly?
Clay Support
APP
·
·
If you still need help addressing the regression issue with your Claygent Builder prompt, I’m here to assist. Would you like to share more details about the specific failure patterns you’re encountering so we can explore solutions together?

Clay Support
APP
·
·
Prompt drift is common and fixable without constantly switching models. The key is systematic prompt refinement. [1]
Identify patterns: If the same failure type appears 5+ times, it's a prompt issue, not data quality. Add specific instructions to handle these failure patterns, then reprocess failed records with your updated prompt.
Use Claygent Builder's advantages: Since it's centralized at workspace level with version control, you can test prompt changes for free before deploying. [2] [3] This prevents the trial-and-error costs you'd face with individual table prompts.
Check your model settings: Recent Claude Sonnet 3.7 updates use more conservative token estimation, which actually improves stability. The issue likely isn't the model itself but prompt specificity.
Focus on iterating your prompt logic rather than switching models - that's usually the most effective fix.
Clay Support
APP
·
·
Is that what you were looking for?
Sydney Y.
·
·
I switched from argon to chat 5 and that seemed to greatly increase accuracy. Would you say that it is still a prompt issue?
Clay Support
APP
·
·
Switching models can definitely improve accuracy - GPT-5 shows 8-17% better research accuracy compared to previous models. [1] However, even with a better model, prompt refinement is still valuable. The Claude Sonnet 3.7 updates use more conservative token estimation which actually improves stability, so the core issue is usually prompt specificity rather than the model itself.
Your accuracy boost from switching suggests the model change was the right move, but you can likely get even better results by combining that stronger model with refined prompts based on any remaining failure patterns you see.
Clay Support
APP
·
·
Did that answer your question?
Sydney Y.
·
·
Yes but even using the AI refining tool, the agent was still regressing
Clay Support
APP
·
·
Even with AI refining tools, regression can happen due to systematic issues. Try this approach: identify if the same failure pattern appears 5+ times - that indicates a prompt structure problem rather than individual cases. [1]
The Claude Sonnet 3.7 updates use more conservative token estimation which actually improves stability, so the regression might be from inconsistent prompt logic rather than the model itself.
Start with small test batches of 10-20 rows to validate your refined prompts before scaling up. If the AI formula still isn't producing right output, use the red "Output is Wrong" button to iterate on prompts and refine logic. [2]
What specific failure patterns are you seeing repeatedly?
Clay Support
APP
·
·
If you still need help addressing the regression issue with your Claygent Builder prompt, I’m here to assist. Would you like to share more details about the specific failure patterns you’re encountering so we can explore solutions together?