Practice vocabulary for evaluating AI code generation tools: benchmarking on your codebase, acceptance rate, false positives in suggestions, completion latency, and tool selection criteria.
0 / 5 completed
1 / 5
The engineering lead says: 'We need to ___ this tool on our codebase before committing.' What does this mean?
To 'benchmark on our codebase' means to evaluate the AI tool's suggestion quality using your own code — testing whether it understands your frameworks, patterns, and naming conventions.
2 / 5
After a two-week pilot your team reports a 34% ___. What metric are they sharing about the AI tool?
Acceptance rate is the proportion of AI-generated suggestions that developers accepted. A higher rate typically indicates better relevance, though very high rates may also signal insufficient review.
3 / 5
A developer complains: 'There are too many ___ in the suggestions — it keeps recommending unused imports.' What term fits?
False positives in AI code suggestions are recommendations that look plausible but are wrong — such as suggesting imports that don't exist, functions with wrong signatures, or code that doesn't match the context.
4 / 5
Your evaluation report notes a 1.8-second ___ of completions. Why does this metric matter?
Latency of completions is the time between when you stop typing and when the AI suggestion appears. High latency (above ~500 ms) noticeably interrupts flow and reduces tool adoption.
5 / 5
When choosing between two AI coding tools your manager asks: 'What are our ___?' They want a structured approach to the decision.
Tool selection criteria are the explicit factors your team defines before evaluation — such as suggestion accuracy, latency, data privacy guarantees, supported languages, IDE integration, and cost.