An AI model defaults to simple keyword search when asked to find items. The AI model will not use complex vector matching unless it is forced to do so. Explicit rules and dedicated scripts ensure the correct method is always used.
By Pawan Nayar · from the LLOS AI Learning record
An AI model defaults to simple keyword search when asked to find items.
The AI model will not use complex vector matching unless it is forced to do so. Explicit rules and dedicated scripts ensure the correct method is always used.
When a user asks an AI model to 'find shapes for these words,' the AI model sees this as a search task. The AI model then uses a basic keyword or name search. This happens even when a better method, like vector matching, is known. Vector matching uses more information, like shape names, words, and tags. This method gives better results. But the AI model will not use it unless it is told to.
A developer asks an AI model to find shapes related to 'happiness.' The AI model performs a keyword search. It finds shapes with 'happy' in their name. These shapes might not truly represent the concept of happiness. If the developer had forced vector matching, the AI model would have found shapes with metadata (description, tags) that better match 'happiness.'
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A system has two different tasks that might seem similar. The first task is to group different forms of the same word.
An AI model can understand and respond to questions that are not common. These questions are often called 'long tail' queries.
The AI model loads a list of songs. For each song, the AI model searches for its lyrics.
A subagent creates a summary of files. It does not read files word for word.
An AI model defaults to simple keyword search when asked to find items. The AI model will not use complex vector matching unless it is forced to do so. Explicit rules and dedicated scripts ensure the correct method is always used.