Gains:
- Risk-based classification of where AI is accelerating and where it is risky in food engineering
- Ability to recognize the risk of LLMs hallucinating food safety, nutrition and legislation
- Validate AI output with laboratory, food safety principles and legislation, and be able to apply ethical principles
It's Friday afternoon, you're in the R&D office of a dairy factory. The marketing team wants a draft label for "sugar-free, probiotic drinkable yoghurt" by Monday morning. An engineer excitedly asks an AI chat tool: "How many mg of aspartame can I put in this product, what is the Turkish Food Codex limit?" Within seconds the model gives a confident answer: "The maximum limit for aspartame is 600 mg/kg, permitted according to Codex article 12/3." The answer looks smooth, convincing and professional. The only problem: the ingredient number is fictitious, the limit varies by product category, and the use of sweeteners in fermented dairy products has its own restrictions. This unit describes exactly this moment, how to manage the “confident but unverified” output of AI.
One central principle will recur throughout this module: the AI output is a proposal, a blueprint, a starting point; The laboratory result can never be used as a basis for the final decision, label or production without being verified with food safety principles and official legislation (Turkish Food Codex). You can think of the AI like a senior intern: fast, productive, well-read, but without signature authority. As the engineer, you verify its draft and sign it.
What does LLM do and what does it not do?
Large language models (LLM) are systems trained on the principle of "predicting the next word" on huge text data. This makes them exceptional at certain jobs and dangerous at others.
LLM does well
LLM makes you unreliable
Text drafting, procedure writing, summarizing
Precise numerical limit and dose recall
Idea generation and brainstorming
Quoting current legislation article
Simplifying complex text, translation
Making error-free nutritional value calculations
Creating a structured table/template
Making security decisions specific to specific products
Checklist and question generation
Source and reference accuracy
LLM "doesn't know what it doesn't know". If he does not remember a contribution limit, he does not leave it blank; fabricates a number that seems statistically reasonable. This is called hallucination and is the riskiest behavior in food engineering.
Hallucination: three food-specific dangers
In the food domain, hallucination occurs in three critical forms:
- Fitting additive limit/dosage: The model may give a preservative limit of "500 mg/kg" when it is actually 150 mg/kg. This is a direct consumer health and legal violation risk.
- Fabricated legislation article: Produces references that do not actually exist, such as "Turkish Food Codex Regulation article 7/b". The number looks realistic but cannot be verified.
- Incorrect nutritional value: incorrectly calculates the protein, energy or sugar value per 100 g of product; This leads to label and nutrition claim errors.
Attention: Any numerical limit, Codex item number or nutritional value given by an LLM cannot be used without confirmation from the original official source (Turkish Food Codex relevant communiqué/regulation, publication of the Ministry of Agriculture and Forestry of the Republic of Turkey). Just because the model looks "sure" doesn't mean it's accurate.
Risk-based division of labor
Classifying AI along a risk axis of where it can be used freely and where strict validation is required is the approach that works best in practice.
Quest
Risk level
AI role
Mandatory verification
Product name/concept idea
low
freelance producer
Marketing review
Procedure/SOP draft
low-medium
draft writer
Quality manager approval
Sensory test questionnaire draft
low
assistant
R&D control
Additive dosage
high
Suggestion only
Codex limit + laboratory
HACCP critical control point (CCP)
very high
Just a list of ideas
HACCP team + decision tree
Allergen declaration
very high
draft
Prescription traceability + legislation
Heat treatment parameter (F-value)
very high
Suggestion
Process authority + validation
The rule is simple: the more the output directly touches consumer health, regulatory compliance or production safety, the more imperative human verification is.
Weak prompt / Strong prompt
The same need, when asked in two different ways, gives very different quality of results.
WEAK PROMPT: "Which preservative should I put in yogurt and how much?" Problem: The model invents an exact dose, does not give a source, does not take into account the product category, and you do not know the point of verification.
STRONG PROMPT: "List the potential protective CATEGORIES for the fermented milk product (drinkable yoghurt) and the function of each. DO NOT RECOMMEND the exact dose; instead add a note for each additive 'From which Turkish FoodCodex notification the limit should be confirmed'. Format the output as a verification checklist. Where in doubt write 'confirmation from source' required." Why it is strong: Do not try to fit the model, but structure it It embeds the verification step in the output. The decision remains with the engineer.
The difference is that instead of making the model "decide", you "make it draft and draw the verification path".
Ethics, trade secret and formula confidentiality
The ethical dimension of AI use is as critical as its technical dimension. As an engineer, one of his company's most valuable assets are its secret formulas and recipes.
ETHICS CHECKLIST (before writing the prompt):[ ] Do I write secret/patented formula rates on this prompt?[ ] Do I share customer-specific specifications?[ ] Do I know the data usage policy of the tool?[ ] Is there a risk of transferring the output to production/label without verification?[ ] Am I making a decision that affects consumer health?
Typing exact prescription rates into a public AI tool could mean the trade secret goes to third-party servers. It's a good habit to ask by abstracting away confidential information ("A contributes X%, B contributes Y%)."
Tip: Keep your prompts "public/anonymous." Use variable (X, Y) instead of actual rates; Use the model for logic and structure, keeping the hidden numbers in your own spreadsheet. This way you get ideas and keep the secret.
mini case
An intern engineer at a snack manufacturer asked AI for a nutritional table for the label of a new cereal bar. The model quoted "8g protein, 180kcal" for the 40g bar, and the chart looked professional. Before approving, the quality manager had the team manually calculate the recipe: the actual value was 5.2 g protein and 165 kcal. The AI predicted the “average” of similar products; This is not the actual recipe for the product. If the chart were printed without verification, the nutritional misrepresentation would be both a legal violation and a loss of consumer confidence. After this incident, the team added the rule "AI nutritional table = draft, calculation from recipe = actual" to the written procedure.
Common mistakes
- Putting it on the report/label without verifying the contribution limit or Codex item number given by the AI.
- Mixing the model's "assured" and fluid tone with accuracy.
- Writing secret formula ratios into public AI tools.
- Leaving high-risk decisions (CCP, allergen, heat treatment) entirely to AI.
- Relying on AI prediction instead of calculating nutritional value from the recipe.
- Accepting a single prompt answer as the only correct answer and not looking for an alternative/source.
- Skipping the verification step by saying "I'll do it later" and moving on to production under time pressure.
In summary
- AI is a powerful accelerator in food engineering, but it is a senior intern, not a decision maker.
- LLMs can produce hallucinations of precise limit, regulatory substance, and nutritional value; Fluency is no guarantee of accuracy.
- Classify tasks based on risk: use freely in low risk, in high risk (dose, CCP, allergen, heat treatment) always verify with human + legislation + laboratory.
- The powerful prompt forces the model not to make a decision, but to produce a draft and show the way to verification.
- Maintain trade secret and formula confidentiality; Work with anonymous variables instead of real rates.
- Every AI output is just a draft until confirmed with the official source and laboratory.
Application task
Identify 8 different tasks in your (or an imaginary) food business where you could get help from AI in the last month. Place each task on the risk-based table in this unit: low, medium, high, or very high risk. For each high and very high risk task, write in a table which official source (Turkish Food Codex relevant communiqué), which laboratory test and with which expert approval the AI output should be verified. Finally, create a 5-item list of “AI output validation rules” for your team to use and ensure at least one of them is trade secret/confidential related.