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Nova Elvaris
Nova Elvaris

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How I Debug AI Hallucinations: A 5-Step Workflow

#ai #programming #productivity #beginners

The function looked perfect. Clean types, good naming, solid error handling. One problem: the library it imported didn't exist.

Not "wrong version." Not "deprecated." The package @utils/deep-validate has never existed, in any registry, ever. The AI made it up.

Welcome to hallucination debugging. Here's the workflow I use to catch these before they waste my afternoon.

Step 1: Run It Before You Read It

This sounds obvious, but I watch developers carefully read AI-generated code, mentally trace the logic, nod approvingly — and then discover it doesn't compile.

Don't review first. Run first.

# For generated code: try to compile/run immediately
npx tsc --noEmit
# or
node script.js
# or
python -c "import generated_module"
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Hallucinations often surface as import errors, undefined references, or type mismatches. A 5-second compile catches what 5 minutes of reading might miss.

Step 2: Verify Every Import and External Reference

This is where most hallucinations hide. The model generates plausible-sounding:

  • Package names that don't exist
  • API methods that aren't real
  • Function signatures that are close but wrong
  • Config options that were removed two versions ago

My checklist:

For each import/require:
  [ ] Package exists in registry (npm, PyPI, etc.)
  [ ] Method/function exists in that package
  [ ] Signature matches current version
  [ ] No deprecated APIs used without awareness
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I keep a terminal open and spot-check as I go:

# Quick existence check
npm info @utils/deep-validate 2>/dev/null || echo "DOES NOT EXIST"
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Step 3: Check the "Confident but Wrong" Spots

Hallucinations are sneaky because the model doesn't signal uncertainty. It writes a hallucinated function call with the same confidence as console.log. Look hardest at:

API surface areas. The model often knows a library exists but confuses methods between versions or similar libraries. fs.readFile vs fs.promises.readFile vs fsExtra.readFile — it'll pick one confidently, and it might be wrong.

Default values and edge cases. "This function returns null when the user isn't found" — does it? Or does it throw? The model guesses based on patterns, not documentation.

Environment-specific behavior. "This works on Node 18+" — does it? Models frequently mix up which features landed in which version.

Step 4: Ask the Model to Verify Itself

This is counterintuitive but effective. Give the model its own output and ask it to find problems:

Here's code you generated. Before I use it:
1. List every external dependency and confirm it exists
2. Flag any API calls you're less than 95% confident about
3. Identify assumptions about the runtime environment
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Models are better at reviewing code than generating it, even their own. I catch 1-2 additional issues per session with this step.

Step 5: Build a Hallucination Log

Every time I catch a hallucination, I log it:

## Hallucination Log

### 2026-03-28
- Model: Claude
- Type: Non-existent package
- Detail: Generated `import { validate } from '@utils/deep-validate'`
- Fix: Used `zod` for validation instead
- Pattern: Model invents utility packages for common tasks

### 2026-03-25
- Model: GPT-4
- Type: Wrong API signature
- Detail: `fs.readFile(path, 'utf8', callback)` — used callback style in async context
- Fix: `await fs.promises.readFile(path, 'utf8')`
- Pattern: Mixes callback and promise APIs for Node.js fs
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After a month of logging, patterns emerge. The model hallucinates differently depending on the domain:

  • Package names: Invents utility libraries (~40% of hallucinations)
  • API signatures: Close but wrong, especially across versions (~30%)
  • Config options: Deprecated or fictional settings (~20%)
  • Logic: Actual wrong algorithms (~10%)

Knowing the distribution tells you where to focus your verification energy.

The Meta-Lesson

AI hallucinations aren't random. They follow patterns — and patterns are debuggable. Treat hallucination detection like you'd treat any other testing discipline: systematic, logged, and improving over time.

The goal isn't to stop using AI because it hallucinates. It's to build a workflow where hallucinations get caught in the first 30 seconds instead of after you've built three more features on top of them.

Quick Reference

Step Time Catches
Run before reading 5 sec Compile errors, missing deps
Verify imports 2 min Fake packages, wrong APIs
Check confident spots 3 min Version mismatches, wrong defaults
Self-review prompt 1 min Subtle logic issues
Log the hallucination 1 min Future pattern recognition

Total: about 7 minutes per AI generation. Saves hours of debugging code built on hallucinated foundations.

What's the most creative hallucination you've encountered? I once got a model to generate a complete REST client for an API that existed only in its training data dreams.

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