An AI model's output must be consistent across all its parts. Different parts of a system must use the same terms for the same things. All numbers and counts must match everywhere they appear.
By Pawan Nayar · from the LLOS AI Learning record
An AI model's output must be consistent across all its parts.
Different parts of a system must use the same terms for the same things. All numbers and counts must match everywhere they appear.
An AI model can become inconsistent when its design documents do not match. For example, one document might say there are 25 chapters. Another document might say there are 20 chapters. This difference creates confusion.
A developer is building an AI learning system. One document says a chapter has 10 concepts. Another document says the same chapter has 21 pages. The developer does not know if a 'concept' is the same as a 'page'. This makes it hard to build the system correctly.
Resolved from this lesson's own record — real definitions, not invented for this page.
More from the same record, pulled by shared topic — not written new for this page.
A project's test plan and all rules must be kept inside the project folder. This makes sure everyone working on the project can find them easily.
Record feedback in a log. Each entry needs a date.
A developer should not skip project documents. These documents include specifications, architecture notes, and format definitions.
Create an append-only log file. Each entry needs a date, a summary, the feedback itself, and the action taken.
An AI model's output must be consistent across all its parts. Different parts of a system must use the same terms for the same things. All numbers and counts must match everywhere they appear.