Stop AI Hallucinations: The Complete Guide

What Are AI Hallucinations in Code?

An AI hallucination is when a Large Language Model (LLM) generates code that looks correct but contains fictional elements: APIs that don't exist, deprecated patterns, phantom dependencies, or invented method signatures.

Real Example: Stripe API Hallucination

// AI-generated code (looks plausible)
const session = await stripe.checkout.createPortalSession({
  customer: customerId,
  return_url: 'https://example.com/dashboard'
});

// ❌ HALLUCINATION: This method doesn't exist
// ✅ CORRECT: stripe.billingPortal.sessions.create()

The AI invented a method name that "sounds right" but doesn't match the actual Stripe API. This compiles, but crashes at runtime.

Why AI Hallucinations Happen

1. Lack of Context = Confident Guessing

LLMs are prediction engines. When you prompt "build a user authentication system" without specifying your stack, database schema, or API contracts, the model fills in the blanks based on statistical patterns from its training data.

Without Context:

"Build auth" → AI guesses you're using Firebase, invents field names, assumes JWT structure

With Context:

"Build auth using Supabase, schema: users(id uuid, email text, role enum), JWT claims: userId + role" → AI has constraints

2. Training Data Lag

Most LLMs are trained on outdated Stack Overflow posts and GitHub repos. They don't know:

✗ That React 18 deprecated componentWillMount()
✗ That Next.js 14 changed the App Router API structure
✗ That your company's internal library was refactored 3 months ago

3. Vibe-Driven Prompts

Prompts like "make it work" or "add user login" are too vague. The AI interprets them based on the most common patterns it's seen, which may not match your architecture.

Common Hallucination Patterns

Phantom Dependencies

import { validateUser } from '@auth/helpers';
// ❌ This package was never installed

AI invents a helper library that sounds logical but doesn't exist in your node_modules.

Deprecated Patterns

class Dashboard extends React.Component {
  componentWillMount() { ... }
  // ❌ React 18 removed this lifecycle method
}

Training data from 2020 doesn't reflect modern framework conventions.

API Method Invention

const data = await db.users.findByEmail(email);
// ❌ Your ORM doesn't have 'findByEmail'
// ✅ It's: db.users.findUnique({ where: { email } })

AI creates methods that would be convenient but aren't in the actual API.

The Fix: Context Engineering Framework

Context Engineering is the practice of giving AI strict constraints upfront so it can't hallucinate. Think of it as "defensive prompting" backed by specs.

The 3-Layer Defense

Spec-First Development

Define API contracts, database schemas, and architecture before prompting AI. No specs = no guardrails.

Generate Full Spec Pack

Lock Context with Rules

Use .cursorrules or system prompts to enforce your exact stack, library versions, and coding patterns.

Generate Cursor Rules

Define "Done" with Acceptance Criteria

Without explicit Given/When/Then tests, AI doesn't know when to stop generating. Give it concrete exit criteria.

Build Acceptance Criteria

Before vs. After: Real Example

Before (Vibe Coding)

Prompt: "Add user authentication"

Result: AI hallucinates:

• Fictional auth library
• Invented DB fields
• Deprecated session logic

After (Context Engineering)

Prompt: "Implement auth using:
- Supabase Auth
- Schema: users(id, email, role)
- JWT claims: userId + role
- Follow .cursorrules patterns"

Result: AI generates:

• Correct Supabase calls
• Schema-aligned logic
• Zero hallucinations

Ready to Stop Debugging AI Hallucinations?

Run a free Spec Readiness Score to see exactly which context is missing from your project.

Diagnose Context Gaps (Free)

The Bottom Line

AI doesn't hallucinate because it's broken. It hallucinates because you gave it a vibe instead of a spec. Context Engineering flips the script: instead of debugging AI-generated code, you prevent hallucinations from happening in the first place.

Key Takeaways

Missing context = confident hallucinations
Spec-first development constrains AI output
.cursorrules and acceptance criteria lock the context
Prevention beats debugging every time

Stop AI Hallucinations: The Context Engineering Guide