Idiomatic Code Pattern Generation Prompt

This prompt is for SDK engineers, DevRel teams, and technical writers who need to generate code examples that feel native to a specific language ecosystem. The job-to-be-done is producing a code snippet that follows community-standard conventions—naming, library usage, project structure, and error handling—rather than a generic, cross-language lowest-common-denominator style. Use it when you are documenting a method, endpoint, or pattern and the example must pass a 'looks like idiomatic [LANGUAGE]' review by an experienced developer in that community.

The ideal user provides a precise functional specification (what the code should do), the target language and version, and a reference to the relevant style guide or popular open-source project that defines 'idiomatic' for that ecosystem. Required context includes the standard library version, preferred package manager, and any framework conventions. Do not use this prompt when you need a runnable, tested example across multiple languages—that is the Multi-Language Code Example Generation Prompt. Do not use it when the primary goal is security hardening or error-handling completeness alone; those have dedicated playbooks. This prompt is specifically for enforcing language-specific style and convention.

Before wiring this into a documentation pipeline, define your evaluation criteria against a living style guide (e.g., PEP 8 for Python, Effective Go for Go, Rust API Guidelines). The output should be a single code block with minimal surrounding prose. If the generated code would fail a linter or idiom check, route it to a repair loop with the specific violations. For high-risk surfaces like authentication or financial operations, always layer on the Security-Safe Code Sample Generation Prompt and require human review before publication.

This prompt template is designed to generate code that adheres to community-standard patterns, naming conventions, and library usage for a specific language or framework. Unlike generic cross-language examples, the output must reflect how experienced developers in that ecosystem actually write code—including import style, error handling idioms, type annotations, and project structure conventions. The template uses square-bracket placeholders that you replace with concrete values before sending to the model. Each placeholder represents a required input that shapes the output's correctness and style.

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You are an expert [LANGUAGE] developer who writes idiomatic, production-quality code following the latest community conventions and style guides.

Generate a [CODE_TYPE] that accomplishes the following task:

[TASK_DESCRIPTION]

Context about the codebase and conventions:
[CODEBASE_CONTEXT]

Constraints:
- Use [FRAMEWORK_OR_STDLIB_VERSION] as the primary dependency.
- Follow the [STYLE_GUIDE_NAME] style guide strictly.
- Handle errors using [ERROR_HANDLING_PATTERN].
- Use [NAMING_CONVENTION] for variables, functions, and types.
- Include type annotations where [TYPE_ANNOTATION_RULE].
- The code must be [SELF_CONTAINED_OR_INTEGRATED].

Output format:
Return a single fenced code block with the language tag. Before the code block, include a brief comment explaining the pattern choice. After the code block, list any assumptions made about the runtime environment.

Examples of idiomatic [LANGUAGE] code for reference:
[FEW_SHOT_EXAMPLES]

Anti-patterns to avoid:
[ANTI_PATTERNS]

To adapt this template, replace each square-bracket placeholder with concrete values. [LANGUAGE] should be the target language (e.g., Rust, TypeScript, Python). [CODE_TYPE] specifies the output shape: function, class, module, script, or test. [TASK_DESCRIPTION] is the precise behavior you want. [CODEBASE_CONTEXT] provides surrounding conventions—existing patterns, linting rules, or team preferences. [FRAMEWORK_OR_STDLIB_VERSION] pins the dependency version to prevent stale examples. [STYLE_GUIDE_NAME] references a specific guide like PEP 8, Google Java Style, or StandardJS. [ERROR_HANDLING_PATTERN] forces a consistent approach: Result types, try/catch, or panic-on-unrecoverable. [FEW_SHOT_EXAMPLES] should contain 2–3 real snippets from the target codebase or popular open-source projects that demonstrate the desired style. [ANTI_PATTERNS] explicitly lists what the model must avoid—such as bare except clauses, unchecked unwrap calls, or mutable default arguments. For high-risk domains like authentication or cryptography, add a [SECURITY_CONSTRAINTS] placeholder that mandates specific safe patterns and requires human review before merging the generated code into production.

After copying the template, test it with at least three different [TASK_DESCRIPTION] values that vary in complexity. Run the output through a linter configured with the target style guide and verify it compiles or passes syntax checks. For SDK documentation use cases, validate that the generated code actually executes against the documented API version. If the model produces code that violates a listed anti-pattern, add that violation to the [ANTI_PATTERNS] list and retry. When the prompt is part of a CI pipeline, store the generated code alongside a hash of the prompt version so reviewers can trace which prompt produced which example.

This prompt is designed to be embedded in a documentation generation pipeline, not used as a one-off chat interaction. The harness wraps the LLM call with pre-processing (language detection, style guide injection), post-processing (compilation checks, linting, style scoring), and a retry loop for outputs that fail validation. Because the prompt generates code that will be published to developers, the harness must treat every output as untrusted until it passes automated checks.

Integration flow: (1) Accept an API operation description, target language, and optional style guide reference as input. (2) Resolve the language-specific style guide from a curated registry (e.g., PEP 8 for Python, Effective Go for Go, Rust API Guidelines for Rust). Inject the guide's key rules into the [STYLE_GUIDE] placeholder. (3) Call the model with the populated prompt template. (4) Extract the code block from the response using a language-aware fence parser. (5) Run the extracted code through a compilation or syntax validation step appropriate to the language—gcc -fsyntax-only, rustc --check, node --check, or gofmt -e. If compilation fails, increment a retry counter and feed the compiler error back into the model as a correction request. (6) Run a style linter (e.g., ruff, clippy, golint) and compute a style compliance score. If the score falls below a configurable threshold, retry with the linter output appended to the [CONSTRAINTS] block. (7) Run a security lint pass using tools like bandit, semgrep, or gosec to catch hardcoded credentials, unsafe deserialization, or injection vectors. Security failures are hard stops—do not retry, escalate for human review.

Retry and fallback strategy: Set a maximum of 3 retries for compilation failures and 2 retries for style violations. On each retry, append the specific error output to the prompt as additional context inside [CONSTRAINTS]. If the model cannot produce a passing output within the retry budget, log the full trace (original input, all model responses, all validator outputs) and route to a human review queue. For security lint failures, skip retries entirely and escalate immediately. Model choice: Use a model with strong code generation capabilities and long-context windows (e.g., Claude 3.5 Sonnet, GPT-4o). For Go and Rust, prefer models with known strength in those ecosystems. Observability: Log every generation attempt with the prompt version, model, language, compilation status, style score, and security pass/fail. This data feeds a dashboard that tracks per-language pass rates and identifies style guide rules that models consistently violate, informing prompt or guide refinements.

What to avoid: Do not publish generated code without running the full validation pipeline. Do not skip security linting even for 'example' code—developers copy examples verbatim. Do not use this prompt for generating production security-sensitive code like authentication handlers or cryptographic operations without mandatory human review. If the target language lacks reliable automated linting, add a manual review gate before publication. Start with a small set of supported languages where your validation toolchain is mature, then expand.