An AI model can answer many user requests by using a pre-saved collection of information. This collection, called a corpus, reduces the cost of serving users. The quality of the corpus must be very high because errors become harder to fix as the corpus grows.
By Pawan Nayar · from the LLOS AI Learning record
An AI model can answer many user requests by using a pre-saved collection of information.
This collection, called a corpus, reduces the cost of serving users. The quality of the corpus must be very high because errors become harder to fix as the corpus grows.
An AI model can use a 'corpus-first' design. This means the AI model tries to answer user questions using a large collection of saved information. This collection is called a corpus. Serving answers from the corpus costs almost nothing. This saves money compared to generating new answers every time.
A developer creates an AI model. The AI model answers questions about many topics. The developer builds a large corpus of common answers. When a student asks a common question, the AI model gives an answer from the corpus. This saves the developer money because the AI model does not need to create a new answer.
Resolved from this lesson's own record — real definitions, not invented for this page.
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The AI model first creates a list of unique words. This list comes from an earlier check.
A developer can build a four-step process. This process ensures accurate data collection.
A system needs a plan for growth. This plan covers data storage, user access, and how the system changes over time.
A complete design specification provides all necessary details. It lists changes to the user interface.
An AI model can answer many user requests by using a pre-saved collection of information. This collection, called a corpus, reduces the cost of serving users. The quality of the corpus must be very high because errors become harder to fix as the corpus grows.