Learn / AI Training Foundations / Module 3 · 12 min
Evaluating Response Quality
Quality is multidimensional
A response can be correct but irrelevant, relevant but incomplete, clear but factually wrong, or detailed but instruction-breaking. Strong evaluation considers the dimensions the task actually asks you to judge.
Six foundational dimensions
- Instruction following — Did it do what was requested?
- Accuracy — Are factual claims correct?
- Relevance — Does it stay focused on the question?
- Clarity — Can the intended audience understand it?
- Completeness — Does it address all required parts?
- Support / calibration — Are uncertain or strong claims appropriately qualified?
Prompt
Give me two advantages of remote work. Keep the entire answer under 30 words.
Choose the stronger response.
Longer is not automatically better
Extra detail can reduce relevance, violate length constraints, or introduce more opportunities for factual errors.
Partial completion
If a user asks to compare two laptops on price and battery life, and the answer discusses only battery life, the answer may be accurate but incomplete.
Module summary
- Evaluate the dimensions required by the task.
- A single major instruction violation can outweigh polished writing.
- Accuracy alone is not enough.
- More words do not automatically mean higher quality.
Quick check
Which dimension asks whether the response stays focused on the user’s actual request?
Quick check
A response is factually accurate but fails half the requested comparison. What is the clearest issue?
Quick check