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Generative AI Leader Practice Question: A developer is building a code‑generation…
A developer is building a code‑generation assistant using the Codey API on Vertex AI. The assistant should generate Python functions based on natural language descriptions. However, the generated code sometimes contains syntax errors. Which parameter adjustment would MOST directly help reduce syntax errors?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Lower the temperature (e.g., from 0.8 to 0.2)
Reducing temperature makes the model more deterministic, which typically reduces creative but incorrect outputs like syntax errors. Prompt engineering can also help, but adjusting temperature is the simplest direct fix. Increasing max tokens or changing top-k does not directly address syntax correctness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Lower the temperature (e.g., from 0.8 to 0.2)
Why this is correct
Lower temperature makes outputs more conservative and less random, reducing the likelihood of generating invalid syntax.
- ✗
Increase the context window
Why it's wrong here
Context window determines how much input can be processed; it doesn't directly affect output quality.
- ✗
Set top-k to 1
Why it's wrong here
top-k=1 is greedy decoding; it may reduce randomness but can also produce repetitive or incomplete code, not necessarily correct syntax.
- ✗
Increase the max output tokens
Why it's wrong here
Max output tokens only controls length; it doesn't affect correctness.
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