Microsoft GH-300 Bundle

The action "Configure approved content exclusions at the appropriate admin scope" is useful when An organization needs to exclude selected sensitive repository paths from supported Copilot features. Organizational exclusion controls can reduce context exposure in supported surfaces. Under the stated condition A policy owner needs assurance that a vendor's data-handling terms meet internal requirements, instead choose "Review the current product-specific privacy documentation and agreement". Data policies should be established from applicable terms, not generic claims.

The alternative "Apply the selected public-code matching policy centrally" fits another situation: An administrator wants to permit public-code matching suggestions only after a legal review. Matching filters express an organization licensing risk decision. Here the condition is A policy owner needs assurance that a vendor's data-handling terms meet internal requirements; use "Review the current product-specific privacy documentation and agreement" instead. Data policies should be established from applicable terms, not generic claims.

For the different scenario A security team wants Copilot availability restricted without removing all GitHub access, "Configure product-specific Copilot policies and license assignments" could be appropriate. Source-control access and AI assistant entitlement are distinct permissions. The present requirement is A policy owner needs assurance that a vendor's data-handling terms meet internal requirements, which calls for "Review the current product-specific privacy documentation and agreement". Data policies should be established from applicable terms, not generic claims.

For the different scenario A model proposes an obsolete interface after the file changed but context was not refreshed, "Update the editor context and regenerate the suggestion" could be appropriate. Prompt quality depends on the code snapshot visible to the client. The present requirement is An inline completion appears based on code above the cursor, which calls for "Recognize surrounding editor code as completion context". The client uses neighboring code to build a relevant model prompt.

"Account for surrounding file and available project context" targets A user assumes a completion is based only on the current cursor character. Context assembly is richer than the single typed token. That differs from An inline completion appears based on code above the cursor; the required decision here is "Recognize surrounding editor code as completion context". The client uses neighboring code to build a relevant model prompt.

The action "Inspect which open files may contribute context" is useful when A suggestion references a function defined in another open tab. Available open-file snippets can inform the generated completion. Under the stated condition An inline completion appears based on code above the cursor, instead choose "Recognize surrounding editor code as completion context". The client uses neighboring code to build a relevant model prompt.

For the different scenario A developer wants to know why the model has not seen an unopened design document, "Supply the document explicitly through supported context tools" could be appropriate. Unreferenced content is not guaranteed to enter a request. The present requirement is A suggestion references a function defined in another open tab, which calls for "Inspect which open files may contribute context". Available open-file snippets can inform the generated completion.

The action "Recognize surrounding editor code as completion context" is useful when An inline completion appears based on code above the cursor. The client uses neighboring code to build a relevant model prompt. Under the stated condition A suggestion references a function defined in another open tab, instead choose "Inspect which open files may contribute context". Available open-file snippets can inform the generated completion.

The alternative "Update the editor context and regenerate the suggestion" fits another situation: A model proposes an obsolete interface after the file changed but context was not refreshed. Prompt quality depends on the code snapshot visible to the client. Here the condition is A suggestion references a function defined in another open tab; use "Inspect which open files may contribute context" instead. Available open-file snippets can inform the generated completion.

"Inspect which open files may contribute context" targets A suggestion references a function defined in another open tab. Available open-file snippets can inform the generated completion. That differs from A model proposes an obsolete interface after the file changed but context was not refreshed; the required decision here is "Update the editor context and regenerate the suggestion". Prompt quality depends on the code snapshot visible to the client.

The alternative "Supply the document explicitly through supported context tools" fits another situation: A developer wants to know why the model has not seen an unopened design document. Unreferenced content is not guaranteed to enter a request. Here the condition is A model proposes an obsolete interface after the file changed but context was not refreshed; use "Update the editor context and regenerate the suggestion" instead. Prompt quality depends on the code snapshot visible to the client.

The alternative "Account for surrounding file and available project context" addresses A user assumes a completion is based only on the current cursor character. Context assembly is richer than the single typed token. It does not resolve A model proposes an obsolete interface after the file changed but context was not refreshed; the correct choice is "Update the editor context and regenerate the suggestion". Prompt quality depends on the code snapshot visible to the client.

The alternative "Update the editor context and regenerate the suggestion" fits another situation: A model proposes an obsolete interface after the file changed but context was not refreshed. Prompt quality depends on the code snapshot visible to the client. Here the condition is A developer wants to know why the model has not seen an unopened design document; use "Supply the document explicitly through supported context tools" instead. Unreferenced content is not guaranteed to enter a request.

The alternative "Account for surrounding file and available project context" addresses A user assumes a completion is based only on the current cursor character. Context assembly is richer than the single typed token. It does not resolve A developer wants to know why the model has not seen an unopened design document; the correct choice is "Supply the document explicitly through supported context tools". Unreferenced content is not guaranteed to enter a request.

For the different scenario An inline completion appears based on code above the cursor, "Recognize surrounding editor code as completion context" could be appropriate. The client uses neighboring code to build a relevant model prompt. The present requirement is A developer wants to know why the model has not seen an unopened design document, which calls for "Supply the document explicitly through supported context tools". Unreferenced content is not guaranteed to enter a request.

The alternative "Supply the document explicitly through supported context tools" fits another situation: A developer wants to know why the model has not seen an unopened design document. Unreferenced content is not guaranteed to enter a request. Here the condition is A user assumes a completion is based only on the current cursor character; use "Account for surrounding file and available project context" instead. Context assembly is richer than the single typed token.

The alternative "Recognize surrounding editor code as completion context" addresses An inline completion appears based on code above the cursor. The client uses neighboring code to build a relevant model prompt. It does not resolve A user assumes a completion is based only on the current cursor character; the correct choice is "Account for surrounding file and available project context". Context assembly is richer than the single typed token.

"Inspect which open files may contribute context" targets A suggestion references a function defined in another open tab. Available open-file snippets can inform the generated completion. That differs from A user assumes a completion is based only on the current cursor character; the required decision here is "Account for surrounding file and available project context". Context assembly is richer than the single typed token.

"Validate generated identifiers against actual project dependencies" targets A completion looks syntactically fluent but calls a nonexistent API. Statistical code generation does not prove API existence. That differs from The same prompt yields different but reasonable code on separate attempts; the required decision here is "Review each candidate instead of assuming deterministic text". Generative model sampling may vary even with similar context.

The action "Distinguish learned patterns from verified live retrieval" is useful when A user asks why an LLM can produce a plausible answer without searching the web. Model text prediction can be persuasive without fresh evidence. Under the stated condition The same prompt yields different but reasonable code on separate attempts, instead choose "Review each candidate instead of assuming deterministic text". Generative model sampling may vary even with similar context.

The alternative "Separate model generation from code execution and testing" fits another situation: An engineer expects Copilot to execute generated code automatically during inline completion. Returning candidate text is not the same as running it. Here the condition is The same prompt yields different but reasonable code on separate attempts; use "Review each candidate instead of assuming deterministic text" instead. Generative model sampling may vary even with similar context.

The action "Separate model generation from code execution and testing" is useful when An engineer expects Copilot to execute generated code automatically during inline completion. Returning candidate text is not the same as running it. Under the stated condition A completion looks syntactically fluent but calls a nonexistent API, instead choose "Validate generated identifiers against actual project dependencies". Statistical code generation does not prove API existence.

The alternative "Provide the rule and test the response against it" fits another situation: A model responds with a general pattern that ignores a hidden business rule. Inference cannot reliably incorporate requirements absent from context. Here the condition is A completion looks syntactically fluent but calls a nonexistent API; use "Validate generated identifiers against actual project dependencies" instead. Statistical code generation does not prove API existence.

The alternative "Distinguish learned patterns from verified live retrieval" addresses A user asks why an LLM can produce a plausible answer without searching the web. Model text prediction can be persuasive without fresh evidence. It does not resolve A completion looks syntactically fluent but calls a nonexistent API; the correct choice is "Validate generated identifiers against actual project dependencies". Statistical code generation does not prove API existence.

The alternative "Provide the rule and test the response against it" fits another situation: A model responds with a general pattern that ignores a hidden business rule. Inference cannot reliably incorporate requirements absent from context. Here the condition is A user asks why an LLM can produce a plausible answer without searching the web; use "Distinguish learned patterns from verified live retrieval" instead. Model text prediction can be persuasive without fresh evidence.

The alternative "Review each candidate instead of assuming deterministic text" addresses The same prompt yields different but reasonable code on separate attempts. Generative model sampling may vary even with similar context. It does not resolve A user asks why an LLM can produce a plausible answer without searching the web; the correct choice is "Distinguish learned patterns from verified live retrieval". Model text prediction can be persuasive without fresh evidence.

For the different scenario An engineer expects Copilot to execute generated code automatically during inline completion, "Separate model generation from code execution and testing" could be appropriate. Returning candidate text is not the same as running it. The present requirement is A user asks why an LLM can produce a plausible answer without searching the web, which calls for "Distinguish learned patterns from verified live retrieval". Model text prediction can be persuasive without fresh evidence.

The alternative "Review each candidate instead of assuming deterministic text" fits another situation: The same prompt yields different but reasonable code on separate attempts. Generative model sampling may vary even with similar context. Here the condition is An engineer expects Copilot to execute generated code automatically during inline completion; use "Separate model generation from code execution and testing" instead. Returning candidate text is not the same as running it.

For the different scenario A completion looks syntactically fluent but calls a nonexistent API, "Validate generated identifiers against actual project dependencies" could be appropriate. Statistical code generation does not prove API existence. The present requirement is An engineer expects Copilot to execute generated code automatically during inline completion, which calls for "Separate model generation from code execution and testing". Returning candidate text is not the same as running it.

"Provide the rule and test the response against it" targets A model responds with a general pattern that ignores a hidden business rule. Inference cannot reliably incorporate requirements absent from context. That differs from An engineer expects Copilot to execute generated code automatically during inline completion; the required decision here is "Separate model generation from code execution and testing". Returning candidate text is not the same as running it.

The alternative "Validate generated identifiers against actual project dependencies" addresses A completion looks syntactically fluent but calls a nonexistent API. Statistical code generation does not prove API existence. It does not resolve A model responds with a general pattern that ignores a hidden business rule; the correct choice is "Provide the rule and test the response against it". Inference cannot reliably incorporate requirements absent from context.

For the different scenario A user asks why an LLM can produce a plausible answer without searching the web, "Distinguish learned patterns from verified live retrieval" could be appropriate. Model text prediction can be persuasive without fresh evidence. The present requirement is A model responds with a general pattern that ignores a hidden business rule, which calls for "Provide the rule and test the response against it". Inference cannot reliably incorporate requirements absent from context.

The action "Review each candidate instead of assuming deterministic text" is useful when The same prompt yields different but reasonable code on separate attempts. Generative model sampling may vary even with similar context. Under the stated condition A model responds with a general pattern that ignores a hidden business rule, instead choose "Provide the rule and test the response against it". Inference cannot reliably incorporate requirements absent from context.