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CodeWhisperer is now part of Amazon Q Developer: AWS says the transition took effect on April 30, 2024. For ordinary inline completions, the useful distinction is that suggestions draw on code and comments available in the IDE; that does not establish that the assistant automatically reads every file in a repository. To tailor recommendations to private organizational code, an administrator must set up a separate customization workflow.
Use the current Amazon Q Developer IDE extension documentation rather than assuming a standalone CodeWhisperer flow still applies.
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How does CodeWhisperer know what I’m trying to write?
A suggestion is shaped by the code and comments available as you work in the IDE. AWS describes the service as analyzing code and comments, and says it can interpret English-language comments to produce code suggestions, including functions and logical blocks. Nearby imports, classes, functions, and a partial implementation can therefore give a completion more useful context than an isolated prompt.
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Does CodeWhisperer read my whole codebase?
The available AWS descriptions support a narrower claim: ordinary inline suggestions use code and comments available in the IDE. They do not establish that inline completion automatically ingests or understands every file in the whole repository. Do not assume that a project-wide convention or private API will be known unless it is present in relevant context or supplied through a separately configured customization.
AWS documents a distinct organization workflow for adapting recommendations to private repositories. That separation matters: using a completion in an IDE and configuring an organizational customization are not the same feature or data path.
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What should I put in comments to get better suggestions?
Give the model a small, coherent task and the code needed to carry it out. AWS Prescriptive Guidance recommends building context in the script, using relevant libraries, classes, functions, and a code skeleton, then writing clear natural-language prompts. Keep a script focused and split unrelated functionality into appropriate modules.
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- Include relevant imports and nearby definitions so names and types have context.
- Provide the surrounding function or class structure rather than asking for a large feature in isolation.
- Keep each comment focused on one task; refine it when a suggestion misses an important requirement.
If output is inaccurate, AWS advises checking that the script context and libraries are relevant, that nearby classes and functions relate to the task, and that the prompt is clear and specific. Improving context can help, but it cannot guarantee a correct result.
How do I customize Amazon Q Developer with my company’s code?
Customization is an administrator-managed workflow intended to make recommendations more relevant to an organization’s code and patterns. In a 2023 CodeWhisperer-era walkthrough, AWS described connecting GitHub, GitLab, or Bitbucket through AWS CodeStar Connections, or uploading source to an S3 bucket and providing its URI. The administrator then creates a customization, reviews its evaluation, and activates it for selected team members.
That walkthrough is dated material, not a reliable current click-by-click guide for Amazon Q Developer. Confirm current plan requirements, supported repository sources, languages, console labels, permissions, encryption options, and retention terms in current AWS documentation before configuring a team. Do not assume the older CodeWhisperer screens or limits remain unchanged.
The older walkthrough described an evaluation score and recommended activation at a score of 6 or higher, with categories labeled Very Good (7–10), Fair (4–7), and Poor (0–4). These are figures from that walkthrough, not verified current Amazon Q Developer thresholds; use the current interface and documentation to interpret any present-day evaluation.
Likewise, the 2023 article listed Java, JavaScript, TypeScript, and Python for the customization it described, and discussed customer-managed AWS KMS encryption and deletion of customization data after a job finished. Those statements should not be treated as current language support or retention guarantees without confirmation in current AWS terms.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Inline suggestions and organizational customization: what is different?
| Aspect | Inline suggestions | Organization customization |
|---|---|---|
| Context source | Code and comments available in the IDE | Organizational source connected through a repository integration or supplied as uploaded code, as described in AWS’s 2023 walkthrough |
| Setup and control | Developer supplies relevant code context and comments while working | An administrator creates a customization, reviews it, and activates it for selected users, in the 2023 walkthrough |
| Intended scope | The task and surrounding code in the current editing context | Organization-specific code and patterns represented in the configured customization |
Current supported languages, plan eligibility, and governance terms are not established by the older customization walkthrough as they apply to Amazon Q Developer today. Check the current product documentation for those details rather than inferring them from the inline-completion feature.
Can I trust or accept the generated code?
Review every suggestion for correctness, security, and fit with the surrounding design. AWS documentation advises reviewing suggestions before accepting them and says editing may be needed to achieve the intended behavior. Run the relevant tests and validate generated fixes rather than treating a plausible completion as a verified one.
AWS says suggestions that may resemble open-source training code can be flagged with repository, file, and license information, and users can filter such suggestions. That information is a useful review signal, not a guarantee that every licensing issue will be detected or resolved.
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A 30 November 2023 AWS Security Blog walkthrough describes a manual IDE security-scan flow in which code in open tabs and linked third-party libraries is archived, uploaded to S3, and scanned through CodeWhisperer and CodeGuru. This describes that walkthrough’s scan process; it is not a general statement about how all inline suggestions are handled or about current Amazon Q Developer privacy terms.
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