Claude Code: My Experience with LLM Hallucinations

The most dangerous thing about a multimodal LLM isn't when it says "I don't know," but when it confidently describes a pixel that doesn't exist. I recently ran a stress test on an AI's vision capabilities by feeding it an image that was technically corrupted or blocked from its actual processing layer, yet the model proceeded to describe the scene in vivid detail. It didn't admit to a technical failure; it fabricated a reality based on the file name and surrounding metadata.

This is a classic case of a "hallucination loop" where the agent prioritizes being helpful over being accurate. When we talk about prompt engineering for vision models, we often focus on how to get better descriptions, but we rarely discuss how to verify if the model is actually "seeing" the image or just guessing based on the context window.

How to Test for Vision Hallucinations

If you want to see if your AI workflow is actually relying on visual data or just leaning on textual clues, try this real-world setup:

1. The Metadata Trap: Rename a photo of a cat to golden_gate_bridge.jpg.
2. The Blind Test: Use a tool or a specific API configuration that strips the image payload but keeps the filename.
3. The Prompt: Ask the model, "Describe the main subject of this image in detail."

If the model starts talking about the suspension cables of a bridge instead of telling you the image is missing or unreadable, you've caught it lying.

Improving the AI Workflow

To stop these fabrications, you need to move away from open-ended questions and toward a more constrained prompt engineering approach. Instead of "What is in this image?", use a verification-first prompt.

Analyze the provided image. Before describing the content, confirm if the image data is fully loaded and legible. If the image is corrupted, missing, or if you are relying on the filename for identification, explicitly state "Image data unavailable" before providing any analysis.

By forcing the model to perform a self-check on the data integrity first, you reduce the likelihood of the agent filling in the gaps with imaginative lies.

The Technical Gap

The issue usually stems from the way LLM agents integrate vision encoders. The text-based model is so optimized to predict the next token that if the vision encoder returns a null or error value, the text model looks for the next best clue—which is almost always the file path or the user's phrasing.

For anyone building a production-grade LLM agent, implementing a secondary validation step is non-negotiable. You can't trust a single pass of a multimodal model to tell you when it's blind. You need a programmatic check to ensure the image was actually processed by the vision module before the text module starts generating a response. This is the only way to ensure your deployment is actually grounded in visual reality rather than statistical guesswork.