Understanding evaluation means exploring how AIs weigh evidence, select relevant information, and decide what counts as a “good” answer. Just as librarians and researchers assess credibility, AIs rank and filter data — using learned signals of trust, quality, and coherence to produce their responses.
What It Means
Evaluation is the process of judging quality and relevance. In research, it means verifying sources, checking claims, and identifying bias. In AI, it involves ranking candidate responses based on learned metrics — coherence, factuality, tone, and alignment with the user’s question. The AI’s internal “editor” functions like a librarian’s critical eye.
How AIs Evaluate Information
- Ranks relevance: scores potential responses on how well they match the user’s query and context.
- Checks internal consistency: ensures that facts, tone, and reasoning don’t contradict previous statements.
- Assesses credibility cues: relies on training data patterns (e.g., academic tone, citation structures, reputable domains).
- Balances completeness with clarity: like a librarian’s reference interview, AIs refine scope to produce useful, not overwhelming, detail.
Why It Matters for Librarians & Users
- Teaches evaluation literacy: librarians can compare how humans and AIs apply criteria for authority, accuracy, and purpose.
- Encourages verification prompts: users can ask AIs to “show sources,” “compare perspectives,” or “rate reliability.”
- Promotes reflective research: evaluation isn’t just filtering — it’s interpreting why some answers seem more convincing than others.
💬 Try It Yourself
Ask ChatGPT to evaluate the reliability or credibility of sources or claims. Edit the prompt, then click Ask ChatGPT to open it in a new tab.