What practices enhance the quality of suggestions provided by GitHub Copilot? (Select three.)

The correct answer is A. Clearly defining the problem or task C. Using meaningful variable names D. Providing examples of desired output. Three practices directly improve GitHub Copilot suggestion quality. (A) Clearly defining the problem or task - through comments, function names, or chat prompts - gives Copilot precise intent to work from, reducing ambiguous or off-target completions. (C) Meaningful variable…

Apply prompt engineering and context crafting

Question

Options

AClearly defining the problem or task
BIncluding personal information in the code comments
CUsing meaningful variable names
DProviding examples of desired output
EUse a .gitignore file to exclude irrelevant files

How the community answered

(16 responses)

A
88% (14)
B
6% (1)
E
6% (1)

Explanation

Three practices directly improve GitHub Copilot suggestion quality. (A) Clearly defining the problem or task - through comments, function names, or chat prompts - gives Copilot precise intent to work from, reducing ambiguous or off-target completions. (C) Meaningful variable and function names provide semantic signal: Copilot infers purpose from identifiers, so descriptive names guide it toward contextually correct suggestions. (D) Providing examples of desired output in comments or docstrings acts as a specification that Copilot can match against, improving accuracy. Option B is incorrect and a bad practice - personal information in code comments is a privacy risk with no benefit to suggestion quality. Option E is incorrect: .gitignore affects version control, not Copilot's suggestion engine.

Topics

#GitHub Copilot#Prompt engineering#Context crafting#Suggestion quality

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