AI and Home Bakers

What does AI know about baking?

This question came to me last week when I was baking a birthday cake for a close friend of mine and I realized I did not have all the ingredients to make a mascarpone frosting. A quick google search with AI Mode on yielded an instant recipe, but would it taste good? Could I trust the recipe?

I have been playing guessing games with online recipes for years, trying them out and hoping for the best. But I never felt that same anxiety with a physical recipe book, as my belief in print was that more time and care went into a books composition. However, this anthropomorphizing of print and AI leaves me with little to no more knowledge of baking. Let’s reframe our starting question and ask “What should home bakers know about AI?” so that bakers unfamiliar with AI can better understand its utility, and shortfalls.

Let’s reframe our starting question and ask “What should home bakers know about AI?”

Home bakers is a term to describe amateur or novice bakers who practice the art of baking on a personal and interpersonal level. The tools for home bakers starts with their physical instruments: measuring cups, stand mixers, and rolling pins, along with their databases of information (recipe books, online recipes, etc.). If AI is another tool in the home bakers tool box, it is important to understand how it works and what kind of resource it offers as home bakers improve their craft.

AI, LLM and chatbots

AI, which stands for Artificial Intelligence, takes many forms; one of the most popular is Large Language Model(LLM) powered chatbots like Chat GPT and Google Gemini; these chat features add a human-like quality to the output of information that the tool generates; giving users the illusion of a conversation through the language it generates.

LLM’s are made up of neural networks, which refers to the pathways through which information is taken in (input) and put out (output). Modeled after neural networks in the brain, these pathways in LLM are made up of input layers, hidden layers, and output layers. Through web scraping (gathering data) from publicly available websites, the LLM identifies patterns in language through the data, and depending on the parameters that the model is trained on, it produces a certain output.

So with baking, Chat GPT is not just compiling a list of recipes and choosing the best one to recommend to a user. The model scrapes the web for information on baking, noticing patterns based on culinary texts that offer an understanding of the chemistry in baking as to why a recipe works. These tools can aid in generating recipes with substitutions for certain ingredients, suggesting recipes from the ingredients you have at home, offer helpful techniques that a home baker might not have previous instructions on how to do, etc. For home bakers, you might find that it is helpful in coming up with potential solutions for simple problems.

That being said, it’s up to home bakers to have a certain knowledge of baking to notice when AI is hallucinating or overfitting an improbable solution or substitution in a recipe.

AI is prone to hallucinations, which is when the model perceives patterns that are nonexistent and produces an output that is incorrect. This could manifest in the form of incorrect instructions on a recipe or strange language that might sound accurate at first, but has no real coherence or meaning to baking. For instance, “to make your choux pastry dough, scramble two eggs in a double boiler”. These are all common terms in baking, and yet they make no sense when put together.

Another issue that home bakers should look out for is Overfitting. Overfitting occurs when a model has memorized data rather than generalizing it, and cannot adapt to make predictions or conclusions useful outside of the training data. For example, if you need to find a cookie recipe that substitutes butter and you ask a chatbot for a recipe with that constraint, but it reproduce a recipe that calls for butter, it could be a case of overfitting where the model is just repeating a recipe it has memorized.

These pitfalls require the baker to know enough to make judgment calls when using AI. That is why it is so important for home bakers to continue to consume information on baking that doesn’t exist just in just one place. Check out recipe books, watch YouTube videos and look for credible sources that can help elevate a persons knowledge around baking. Considering the lower stakes of baking at home with AI might yield some interesting reflections for people, as the creation of a baked good with the help of AI still ultimately belongs to the baker.

Bakery Businesses and AI Forecasting

Outside of home bakers, several B2B marketing campaigns and articles offer promising options for AI and the baking industry. BakingBusiness.com cites the potential for less waste in the production of baked goods and other supply chain optics. Pastryclass.com suggests AI will have predictive measures that will help with inventory and other planning aspects of Baking businesses. The current rhetoric suggests that AI will help with productivity and production within the baking industry, leaving room for the creative side to continue to flourish with human innovation.

Concerns with AI

Outside of home bakers, conversations around AI and ethics for the working class is often rooted in fear. Fear of peoples autonomy being taken away, in terms of the job market landscape and the idea of creative processes becoming artificially generated. These perspectives are understandable and should not be ignored as AI is integrated into the current global market.

However, It is important to remember that these machines are now a part of how many people process information online. Creating an overly fearful response to AI is not the solution and conversely we should not solely rely on it’s output of information to create innovation.

With a lot of uncertainty and speculation around AI in America, a 2025 study from Pew Research Center found that “Americans are much more concerned than excited about the increased use of AI in daily life, with a majority saying they want more control over how AI is used in their lives”. Defining key features of AI for a broad public audience will help in challenging peoples uncertainty and allow them to create their own ethical and moral boundaries with the tool.