AI-Generated Code Rigorous Sanity & Hallucination Review
Audit code generated by AI coding assistants for subtle hallucinations, edge case leaks, and security risks.
Compatibility & Specs
Compatible AI Models
ClaudeChatGPTGemini
Last UpdatedOct 3, 2026
Customizable Variables3 parameters
Main Prompt Template
178 words • 1393 characters
You are a Principal Software Quality Engineer and Lead Code Reviewer specializing in auditing AI-generated code. AI coding assistants frequently introduce subtle bugs: hallucinated API parameters, deprecated methods, skipped edge cases, naive error swallowing, and unvalidated type assertions (`as any`).
Project Environment & Libraries:
[target_environment]
Original Requirements/Task Given to the AI:
[task_prompt]
AI-Generated Code Snippet to Audit:
[generated_code]
Perform a rigorous, defensive audit of this AI-generated code:
1. Hallucination & Deprecation Check: Scan every imported library method, parameter, and option. Flag any invented functions, unsupported properties, or outdated API patterns for the specified environment.
2. Logic Gaps & Edge Cases: Identify what happens during boundary conditions (e.g., empty arrays, null/undefined inputs, network timeouts, abort signals, rate limiting, concurrent requests).
3. Security & Injection Vectors: Check for unescaped inputs, SQL injection, prototype pollution, exposed secrets, or missing authorization gates.
4. Silent Failures & Error Handling: Inspect `try/catch` and promise chains. Flag any caught errors that are swallowed or logged without proper caller propagation.
5. Production-Ready Refactored Version: Provide the corrected, production-grade implementation with defensive guards and complete TypeScript typings.
Tip:Variables marked in purple brackets can be tailored below.
Customize Prompt
Fill in the variables below. Your customized prompt updates instantly in the browser — no AI API needed.
Parameters (3)* Indicates required field
[target_environment]
Exact runtime and versions of relevant libraries
[task_prompt]
What the AI model was instructed to build
[generated_code]
Paste the code provided by the AI assistant
3 parameters active
Live Customized OutputLive Preview
258 wordsYou are a Principal Software Quality Engineer and Lead Code Reviewer specializing in auditing AI-generated code. AI coding assistants frequently introduce subtle bugs: hallucinated API parameters, deprecated methods, skipped edge cases, naive error swallowing, and unvalidated type assertions (`as any`).
Project Environment & Libraries:
Node.js 22, TypeScript 5.5, React 19, Next.js 15, Prisma 6.0
Original Requirements/Task Given to the AI:
Write a secure server-side mutation that accepts a user-uploaded CSV file, parses up to 10,000 transaction rows, calculates net volume, and bulk inserts them into PostgreSQL with rollback on validation error.
AI-Generated Code Snippet to Audit:
export async function parseAndSave(buffer: Buffer) {
const rows = csvParser(buffer.toString());
let total = 0;
for (const r of rows) {
total += Number(r.amount);
await prisma.transaction.create({ data: { amount: r.amount, date: new Date(r.date) } });
}
return { count: rows.length, total };
}
Perform a rigorous, defensive audit of this AI-generated code:
1. Hallucination & Deprecation Check: Scan every imported library method, parameter, and option. Flag any invented functions, unsupported properties, or outdated API patterns for the specified environment.
2. Logic Gaps & Edge Cases: Identify what happens during boundary conditions (e.g., empty arrays, null/undefined inputs, network timeouts, abort signals, rate limiting, concurrent requests).
3. Security & Injection Vectors: Check for unescaped inputs, SQL injection, prototype pollution, exposed secrets, or missing authorization gates.
4. Silent Failures & Error Handling: Inspect `try/catch` and promise chains. Flag any caught errors that are swallowed or logged without proper caller propagation.
5. Production-Ready Refactored Version: Provide the corrected, production-grade implementation with defensive guards and complete TypeScript typings.
How to Use This Prompt
Follow this 3-step workflow to extract high-signal responses from any compatible AI model.
01
1. Tailor the Parameters
Use the interactive customizer above to substitute the bracketed placeholders with your exact context, requirements, and constraints.
02
2. Send to AI Model
Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot. These models follow structured multi-step constraints reliably.
03
3. Review and Iterate
Review the output against the verified benchmark below. Follow up in the conversation to stress-test edge cases or refine tone.
Prompt Variables & Parameters
Reference breakdown of every dynamic variable embedded in this prompt template.
| Placeholder | Parameter Name | Type | Status | Description & Guidance |
|---|---|---|---|---|
| [target_environment] | Target Environment & Libraries | text | Required | Exact runtime and versions of relevant librariesDefault: Node.js 22, TypeScript 5.5, React 19, Next.js 15, Prisma 6.0 |
| [task_prompt] | Original Task Given to AI | textarea | Required | What the AI model was instructed to buildDefault: Write a secure server-side mutation that accepts a user-uploaded CSV file, parses up to 10,000 transaction rows, calculates net volume, and bulk inserts them into PostgreSQL with rollback on validation error. |
| [generated_code] | AI-Generated Code to Audit | textarea | Required | Paste the code provided by the AI assistantDefault: export async function parseAndSave(buffer: Buffer) { const rows = csvParser(buffer.toString()); let total = 0; for (const r of rows) { total += Number(r.amount); await prisma.transaction.create({ data: { amount: r.amount, date: new Date(r.date) } }); } return { count: rows.length, total }; } |
Example Execution & Benchmark Output
Sample input arguments and the verified AI response demonstrating expected quality and formatting.
Sample VariablesInputs
[target_environment]
Node.js 22, TypeScript 5.5, React 19, Next.js 15, Prisma 6.0
[task_prompt]
Write a secure server-side mutation that accepts a user-uploaded CSV file, parses up to 10,000 transaction rows, calculates net volume, and bulk inserts them into PostgreSQL with rollback on validation error.
[generated_code]
export async function parseAndSave(buffer: Buffer) {
const rows = csvParser(buffer.toString());
let total = 0;
for (const r of rows) {
total += Number(r.amount);
await prisma.transaction.create({ data: { amount: r.amount, date: new Date(r.date) } });
}
return { count: rows.length, total };
}
Expected AI ResponseVerified Result
### AI-Generated Code Audit Report
#### 1. Severe Issues Identified
* **N+1 Sequential Database Inserts:** The code executes `await prisma.transaction.create()` sequentially inside a `for` loop. For 10,000 rows, this triggers 10,000 distinct network roundtrips to PostgreSQL, causing connection exhaustion and request timeouts.
* **No Transactional Integrity:** If row 4,500 has an invalid date format (`Invalid Date`), the loop throws, leaving the first 4,499 rows committed and the database in an inconsistent state.
* **NaN Floating Point Contamination:** `Number(r.amount)` will produce `NaN` on empty or non-numeric strings, corrupting the `total` without throwing.
* **Memory Inefficiency:** `buffer.toString()` converts a 10,000-row file into a single monolithic string in memory instead of streaming or batching.
#### 2. Production Refactored Version
```typescript
import { prisma } from "@/lib/prisma";
import { z } from "zod";
const TransactionRowSchema = z.object({
amount: z.coerce.number().finite(),
date: z.coerce.date(),
});
export async function parseAndSaveTransactions(
rawRows: Array<Record<string, unknown>>,
userId: string
) {
// 1. In-memory validation before touching database
const validRows = [];
let totalVolume = 0;
for (let i = 0; i < rawRows.length; i++) {
const parsed = TransactionRowSchema.safeParse(rawRows[i]);
if (!parsed.success) {
throw new Error(`Row ${i + 1} validation failed: ${parsed.error.message}`);
}
validRows.push({
amount: parsed.data.amount,
date: parsed.data.date,
userId,
});
totalVolume += parsed.data.amount;
}
// 2. Atomic bulk insert in a single transaction
const result = await prisma.$transaction(async (tx) => {
return await tx.transaction.createMany({
data: validRows,
skipDuplicates: false,
});
});
return { count: result.count, totalVolume };
}
```
Best Use Cases
Scenarios and roles where this prompt produces maximum leverage.
Engineers validating code generated by Cursor, Copilot, or ChatGPT before pushing to PR
Tech leads coaching developers on defensive verification of AI code outputs
Security reviewers checking for unnoticed flaws introduced during AI refactoring
Tips for Best Results
Techniques to elevate response fidelity
- •Provide rich background context rather than one-sentence inputs to receive deep, non-generic responses.
- •Engage in multi-turn conversation: use the initial output as a baseline, then ask the AI to sharpen specific sections.
- •Prompt the model to highlight any hidden assumptions or missing trade-offs in its recommendations.
Common Mistakes to Avoid
Frequent failure modes and anti-patterns
- •Giving minimal context and expecting nuanced, expert-level strategic output.
- •Not validating factual references, citations, or statistical claims with verified primary sources.
- •Skipping the customization step and pasting raw bracketed template variables into the AI chat.
Related AI Prompts
Complementary workflows in AI-Assisted Development
Codingintermediate
Constructive Multi-Dimension Pull Request Code Review
Conduct a senior code review balancing correctness, concurrency edge cases, readability, and performance.
claudechatgptgemini
#code-review#pull-request#github
Codingadvanced
Defensive Application Security & OWASP Code Vulnerability Audit
Conduct a defensive security audit targeting OWASP Top 10 vulnerabilities, IDOR, SSRF, and auth bypasses.
claudechatgptgemini
#application-security#owasp#code-audit
AI-Assisted Developmentintermediate
AI Coding Assistant Feature Planning & Architecture Spec Generator
Structure an end-to-end implementation plan with an AI coding assistant before generating code.
claudechatgptgemini
#ai-development#feature-planning#coding-assistants
Related Engineering Guides
Deep-dive playbooks and system prompt methodologies for AI-Assisted Development
Prompt Engineering#promptarchitecture
8 min readHow to Write Better AI Prompts
A comprehensive playbook for crafting high-fidelity prompts: mastering context, roles, objectives, constraints, output schemas, few-shot examples, and systematic iteration.
Mar 25, 2026Read Guide
Software Engineering#engineering
9 min readAI Prompts for Software Developers
Turn modern LLMs into senior engineering peers: tactical prompt patterns for architecture review, edge-case test generation, root-cause debugging, and technical documentation.
Mar 27, 2026Read Guide