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How to Reduce Hallucinations in Code Generation

AI-generated code can look convincing while calling a nonexistent method, misunderstanding a project convention, or quietly skipping an edge case. The fix is not simply to ask for “better code.” Treat code generation as a process of gathering evidence, limiting the task, and verifying the result.

Use this step-by-step workflow to make AI coding assistance more reliable, whether you work in an IDE, use Claude Code or Codex, or call a model through an API.

1. Give the model real project context

Many hallucinations start with missing information. Before asking for a change, provide the relevant source files, configuration, and nearby tests. In a coding agent, ask it to inspect the repository first. For an API prompt, include the important excerpts directly or retrieve them with your application’s search tools.

Be specific about the project’s language and versions, the framework, and existing conventions. A request such as “add pagination” leaves major decisions open. “Add cursor pagination to this endpoint, following the response format used in list_orders” anchors the work in actual code.

2. Turn the request into a bounded task

State the expected behavior, the files or interfaces that may change, and what must stay untouched. Include relevant constraints such as backward compatibility, supported runtimes, and whether new dependencies are allowed. Ask for clarification when requirements conflict or essential details are missing.

For example, specify the input, output, error behavior, and empty-result behavior for a function. A narrow, testable request gives the model fewer opportunities to invent requirements.

3. Ask for evidence before implementation

For unfamiliar APIs, libraries, or project-specific helpers, require the model to verify that a symbol exists before using it. It can inspect package files, search the repository, or consult documentation supplied to it. Ask it to identify the source file or documentation that supports any non-obvious claim.

This is especially useful when dependencies have changed between versions. If the model cannot verify an API, have it say so and propose a way to check rather than filling the gap with a plausible-looking method.

4. Generate a small change first

Break large features into reviewable pieces. Start with a short implementation plan, confirm its assumptions, then ask for one coherent change at a time. Prefer edits that follow existing patterns over a new abstraction unless the task genuinely needs one.

Ask the model not to modify unrelated files and to explain any necessary deviation from the plan. Smaller diffs are easier to inspect and make incorrect assumptions easier to catch before they spread.

5. Make tests part of the request

Tell the model which tests to add or update. Cover ordinary behavior, boundary conditions, invalid input, and failure cases that matter to the feature. For a parser, for example, include malformed input and empty input; for a permission check, test both allowed and denied access.

Then run the project’s actual checks: targeted tests, linting, type checking, and, when practical, the full test suite. Do not treat a model’s claim that tests pass as proof. Confirm the commands completed successfully and inspect failures yourself.

6. Review the diff and verify risky details

Read the final diff for invented imports, incorrect assumptions about data shape, missing error handling, and changes beyond the request. Check security-sensitive code carefully, especially authentication, authorization, database queries, shell commands, and handling of untrusted input.

For important behavior, use an independent check: run a reproduction, inspect official documentation, or ask a separate review pass to find defects without rewriting the implementation. Independent verification is more useful than asking the same model to declare its own answer correct.

7. Choose models according to the task and budget

More capable models can help with complex debugging and broad changes, while faster, lower-cost models may be sufficient for small, well-specified edits. Keep the verification steps constant either way: a strong model can still misunderstand an incomplete request.

When API cost makes experimentation expensive, 59API offers pay-as-you-go access to Claude Opus, Sonnet, Haiku and Fable, as well as GPT models. It is compatible with Claude Code, Codex, and OpenAI SDKs using the base URL https://api.59api.com. 59API describes its access as native official-quality models without downgrade, and offers a referral rebate. Developers comparing inference costs can sign up with 59API and test the models against their own coding tasks.

Use a repeatable checklist

• Provide repository-specific context and versions.
• Define expected behavior and constraints.
• Verify unfamiliar APIs against code or documentation.
• Keep changes small and request relevant tests.
• Run checks and review the diff before merging.

These steps cannot eliminate every coding error, but they make unsupported guesses easier to prevent and catch. The key is to treat generated code as a proposal backed by evidence and tests, not as a finished answer.

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