liveBook · Manning

Accuracy

As explained in Build an AI Agent (From Scratch)

Accuracy is the proportion of evaluated answers or predictions that match the correct reference outcomes.

Accuracy is the fraction of evaluated problems answered correctly. In a 20-problem evaluation, a score of 10/20 means 10 answers matched the correct outcomes. Accuracy differs from judged solvability, which counts problems a model attempted. section 2.4.3

A tool-free ceiling

If only 5 of 20 problems are solvable without tools, the expected tool-free accuracy ceiling is 5/20 (25%). section 2.4.3

Table 2.1 Experiment results across models

Model

Accuracy

Judged solvable

GPT-5

10/20 (50%)

11/20 (55%)

GPT-5 mini

4/20 (20%)

8/20 (40%)

Claude Sonnet 4.5

3/20 (15%)

4/20 (20%)

Claude Haiku 4.5

3/20 (15%)

4/20 (20%)

Compare accuracy and judged-solvable counts across the four evaluated models. section 2.4.3

How Build an AI Agent (From Scratch) covers it

How deep it goes
Defines accuracy as correct answers out of 20 and compares model scores, without elaborating further on metric calculation.
Examples
Uses 20 GAIA level-1 problems, a 5/20 tool-free ceiling, and model score comparisons.
What you can do after
Evaluate model performance by comparing correct-answer counts across a fixed set of problems.
How it connects
Contrasts accuracy with judged solvability and discusses calibration, training data, and tool use.

Also explained in

Compare how 32 books explain accuracy →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Build an AI Agent (From Scratch). The section links point to the parts of the book it draws on.