For bulk-fix tasks, an AI model should process each item in one pass. The AI model should compare the item to a rule and give a final answer. This avoids multiple steps and saves time.
By Pawan Nayar · from the LLOS AI Learning record
For bulk-fix tasks, an AI model should process each item in one pass.
The AI model should compare the item to a rule and give a final answer. This avoids multiple steps and saves time.
A developer should use one AI model call for each item. The prompt should ask the AI model to compare the item to a rule. The AI model then gives a final decision. This method is faster and uses fewer resources than multiple steps. It also reduces the need for extra code and user updates.
A developer needs to check 1000 product descriptions for incorrect emojis. The developer creates one prompt for each description. The prompt asks the AI model: 'Is this emoji correct, or should it be changed? Do not change it if the new emoji is very similar to the old one.' The AI model processes each description once and gives a final answer. This is faster than having the AI model suggest changes, then a script filter them, and then the AI model review them again.
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A developer must estimate task time based on the AI model's speed. Do not estimate based on how long a person would take.
Giving an AI model too much information can make it less accurate. This is called 'context rot'.
An AI model should frame calls to action as challenges. This means encouraging the user to achieve a goal.
An AI model should estimate content tasks assuming it can do many things at once. This means reading multiple files or making many edits in one step.
For bulk-fix tasks, an AI model should process each item in one pass. The AI model should compare the item to a rule and give a final answer. This avoids multiple steps and saves time.