Hallucination Triage & Root Cause
A systematic engineering process for analyzing AI model errors: diagnosing context defects, identifying prompt conflicts, and establishing systemic rules instead of mindless query repetition.
1. Concept Overview & Systemic Problem
When an agent outputs a non-existent method or uses a library not present in the project, the typical reaction of an inexperienced vibe coder is anger and emotional outbursts in chat: "Are you stupid?! I said to use Drizzle, not Prisma!"
- This leads to the accumulation of emotional clutter in the chat context.
- The model starts apologizing but continues to make mistakes since the root cause has not been addressed.
Hallucination Triage is a cold engineering approach: treating the model's hallucination not as "AI stupidity," but as a context delivery defect (Context Delivery Bug) that requires classification and systematic correction.
2. Architectural Taxonomy & Mental Model
┌─────────────────────────────────────────────────────────────┐
│ HALLUCINATION TRIAGE MATRIX │
├─────────────────────────────────────────────────────────────┤
│ 1. DETECTION: The model issued a fabricated method or type │
│ • Example: `db.user.findFirstOrThrow()` in Drizzle ORM │
├─────────────────────────────────────────────────────────────┤
│ │ │
│ ▼ ROOT CAUSE DIAGNOSIS │
│ ┌───────────────────────────────────────────────────┐ │
│ │ Type A: Missing file? ➔ The model did not see schema │ │
│ │ Type B: Rule conflict? ➔ Rules mention Prisma │ │
│ │ Type C: Outdated weights? ➔ Model knowledge from 2023 │ │
│ └───────────────────────────────────────────────────┘ │
│ │ │
│ ▼ SYSTEMIC REMEDIATION │
│ 1. Add the exact types file to the prompt │
│ 2. Enforce a rule in rules: "NEVER use Prisma syntax" │
│ 3. Add validation with the compiler `tsc --noEmit` │
└─────────────────────────────────────────────────────────────┘
3. Technical Pipeline & Internal Mechanics
01. Eliminating Hallucinations via Declaration Files
An agent continuously fabricated props for an internal button component. Instead of explanations, the developer created a file src/components/ui/button.d.ts with clear TypeScript types and added it to the automatic context list. Errors disappeared by 100%.
02. Post-Mortem Protocol for AI Bugs
After resolving a complex bug, the engineer spends 2 minutes updating .agents/rules/learnings.md:
"In this project, the Next.js router uses Server Actions without manual fetch calls. Do not generate unnecessary route handlers for simple mutations."
4. Production Engineering Scenarios
- Over-Correction: Attempting to fix one minor hallucination, an engineer writes a massive 50-line rule in all caps that prohibits the model from using useful auxiliary patterns. Rules must be surgically precise.
- Ignoring Model Versions: If the model has too low reasoning capability (e.g., an outdated 3B model), no triage will help it solve complex tasks. Sometimes the only remedy is to switch to a more powerful model.
5. Pitfalls, Common Mistakes & Security
Hallucination is a mirror of the quality of your engineering documentation. If the model hallucinates, it means your system has been insufficiently deterministic. Systematic triage transforms each error into a building block in the foundation of your repository's reliability.
FAQ: Hallucination Triage & Root Cause
Related terms
AI Hallucinations & Confabulations
The generation of factually incorrect, fabricated, or non-existent information (libraries, API methods, quotes) by a language model, expressed with high probabilistic confidence.
Prompt Fatigue
A psychological and cognitive state of exhaustion experienced by developers due to the constant need to translate technical intentions into vague natural language, repeat context, and repeatedly reformulate prompts.
Agent Rules (.cursorrules / CLAUDE.md / AGENTS.md)
Machine-readable files of architectural regulations and constraints in the repository that are automatically mounted into the system context of AI agents to prevent codebase degradation.
Context Rot & Attention Decay
Systemic degradation of accuracy, instruction adherence, and logical consistency in LLMs as dialog noise, outdated code drafts, and compiler outputs accumulate in the working context window.