Does an AI knowledge base get better over time, or just more complicat
<!-- SC_OFF --><div class="md"><p>Say an AI agent has to prepare a weekly market report. After a few weeks, there are two ways to handle it....
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{
"prompt": "<!-- SC_OFF --><div class=\"md\"><p>Say an AI agent has to prepare a weekly market report. After a few weeks, there are two ways to handle it. Keep a library of things it has already learned: report structure, useful prompts, examples, data sources, and rules for how to handle certain cases. Or just give it the latest data and requirements every week and let it start fresh.</p> <p>The library should make the workflow faster and more consistent, but it can also pick up outdated examples or conflicting instructions.</p> <p>Which one works better in practice: building up reusable skills over time, or keeping each run clean and starting from scratch?</p> </div><!-- SC_ON --> &; submitted by &; <a href=\" /u/virginia9224 </a> <br/> <span><a href=\" &; <span><a href=\"",
"model": "Claude 3.7 / GPT-4o",
"aspect_ratio": "1:1",
"category": "Automation",
"content_type": "workflow_template",
"surface": "workflows",
"quality_score": 83,
"provenance": "COMMUNITY_PROMPT"
}Voice Search & FAQs
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