Tech Tuesday: Hallucinations

Tech Tuesday: When AI Goes Off the Rails — Spotting and Fixing Dangerous Recipe Hallucinations

AI is quickly becoming a kitchen assistant—generating recipes, meal plans, and even food-safety advice. But recently it has been generating the wrong kind of headlines: raw-chicken temperatures that are far too low, “creative marinades” involving cleaning chemicals, and improvised instructions that would never pass a basic safety class. 

These failures aren’t random—they have technical roots. Today’s Tech Tuesday explains how these hallucinations happen, why they pose real-world risks, and how to build guardrails that keep dangerous ideas out of the food system.

We touched on this topic before here:  In the news: Aromatic Water Mix, Oreo Vegetable Stir Fry, and Coco Puff Carrot Noodle Salad? and also a review here: AI-Assisted vs AI-Directed -- So now it's definitely time to take a good, deep look at some of the causes and what you can do about it.

How Recipe Models Actually Work (The Technical Core)

Large language models do not “understand” food. They predict the next token—small pieces of text—based on probabilities learned from data. Each step of recipe generation is essentially a calculation:

P(next_token | previous_tokens)

If a model has ever seen an unsafe phrase or poorly written technique, it stores it as a tiny but non-zero probability. Under the right conditions—creative prompts, high sampling temperature, or sparse context—that probability can surface.

Example: A simplified continuation table for “Before cooking chicken, you should…”

  • “…wash your hands with soap” → 0.40
  • “…sanitize the counter” → 0.25
  • “…use separate cutting boards” → 0.30
  • “…rinse the chicken in bleach” → 0.05

That 0.05 probability is small but dangerous. The model does not know bleach is toxic; it only knows it has appeared in some text somewhere. This is how harmful ideas slip into outputs.

Technical Deep Dive: Safety Layers That Stop Hallucinated Recipes

Safe food-AI systems use multiple layers of technical validation. Each one reduces the chance that a bad idea reaches a real kitchen.

1. Ingredient and Chemical Filters

Systems can scan each generated step for non-food items—cleaners, solvents, fuels, medications—and block them immediately. A simple rules engine works:

// Pseudocode for ingredient screening
FOR step IN recipe:
    ingredients = extract(step)
    IF any(ingredients IN FORBIDDEN_ITEMS):
        block_output("Unsafe ingredient detected.")

2. Temperature and Time Sanity Checks

Critical temperatures are well known: chicken (165°F/74°C), ground beef (160°F/71°C), pork (145°F/63°C). If a model suggests anything lower, a safety layer corrects or rejects it:

SAFE_MIN_TEMP_F = { "chicken":165, "turkey":165, "ground_beef":160 }

IF protein IN SAFE_MIN_TEMP_F:
    IF suggested_temp < SAFE_MIN_TEMP_F[protein]:
        suggested_temp = SAFE_MIN_TEMP_F[protein]

3. Retrieval-Augmented Safety (RAG)

Connecting the model to authoritative references—USDA charts, FDA guidelines—forces it to use real values instead of guessing. This narrows the probability space so unsafe numbers cannot appear.

4. Logging and Monitoring “Near Misses”

Professional systems track every unsafe attempt the model makes. Patterns of recurring hallucinations can then be addressed with prompt changes, model fine-tuning, or stricter rules.


Practical Food Connection: What Cooks and Food Pros Should Do

For Home Cooks

  • Treat AI recipes as drafts—verify cooking times and temperatures with trusted sources.
  • Ignore any recipe containing non-food chemicals or household cleaners.
  • Ask direct safety questions separately (“What is the safe internal temp for chicken?”).

For Restaurants and Food Companies

  • Maintain an approved table for ingredients, allergens, and cooking temperatures.
  • Separate AI “idea generation” from production systems.
  • Require human sign-off for anything moving past R&D.

For Developers Building Food AI Tools

  • Add explicit numerical safety checks.
  • Use retrieval to supply real food-safety data.
  • Test models with adversarial prompts to expose blind spots.

The Essential Ingredient: Human Oversight

AI does not understand microbes, toxins, spoilage, or risk. It only understands patterns. That means dangerous hallucinations will occasionally surface in unfiltered outputs. Safe systems rely on a combination of:

  • Technical guardrails (rules, filters, numeric validation, retrieval), and
  • Human oversight to evaluate whether an idea makes sense in reality.

Handled correctly, AI can accelerate menu design and experimentation—but humans must remain fully responsible for food safety. Creativity is welcome. Consequences are not optional.

© 2025 Creative Cooking with AI - All rights reserved.


Comments

Post a Comment