As an AI company, we know that understanding AI can be complicated. Part of what makes it difficult is that AI is not one thing. It's a combination of concepts, technologies and methodologies, with the terms used to describe them often used interchangeably, which doesn't help. If you're evaluating AI for your business, both the opportunities and the risks, or even just trying to make sense of the conversation around it, there are a handful of concepts worth understanding. We will cover these in this article, and lay the groundwork for diving deeper into some AI concepts in future posts.
Large Language Models (LLMs)
Let's start with large language models or LLMs. This is the engine powering almost all current AI tools like ChatGPT, Claude, and Gemini. The breakthrough behind today's AI happened when researchers discovered that if you train these models on enough data with enough computing power, they become remarkably good at predicting sequences of words.
At the risk of oversimplification, that is what today's AI is: a very sophisticated word prediction tool. If you give a model the words "Mary had a little...", an LLM will fill the blank with "lamb" because that's the most common completion of those words within the data used to train the model. Through training, AI models have evolved to follow instructions, answer questions, and work through problems step by step. Almost every AI solution uses these LLMs as their engine.
We will be exploring how and why all AI solutions are not the same, even though they run on mostly the same engines, in this article and future ones (spoiler: you can put a Ferrari engine in a Toyota, but it still won't run like an actual Ferrari).
Training data
Training data is the information that was fed to an LLM during its development, before you ever use the tool. Today's biggest models have been trained on most of the publicly available internet: Wikipedia, books, news articles, forums, academic papers, and more. This is what gives them their general knowledge. But the internet doesn't contain a company's product catalog, pricing rules, or the way their customers actually write orders, i.e. the information that allows AI tools to become useful to a specific business. Those things need to be shared with the AI through context and memory, two concepts we will cover below.
Chatbots
The difference between AI, chatbots and LLMs is one area we see often getting confused. LLMs are the engine. Chatbots are one version of the tools powered by that engine. The most well known chatbots are ChatGPT, powered by OpenAI's GPT models, and Claude, powered by Anthropic's Claude models. In simple terms, chatbots enable users to interact with LLMs through a chat interface and have a back and forth conversation. You can ask a chatbot "what's the best way to manage inventory for perishable goods?" and get a reasonable answer. But you can't hand it 200 incoming orders and expect it to process them into your system. Chatbots are just one type of AI tool, and as we will see, one of the more limited tools when it comes to business use.
Prompts
Prompts are instructions given to an LLM. They come in two main forms. When you write something into ChatGPT, for example "write an email to John," this is a user prompt, coming from you, the user, and changing for every new request. The other type are system prompts, which are the instructions that an AI tool uses every time it receives a request. For example, a system prompt could be: "whenever a user requests to write an email, start with a greeting and end with the user's signature." Or in food distribution: "when a customer mentions a product without a SKU number, match it against their order history before checking the general catalog." Prompts are where developing AI tools gets very complex, and where specific industry expertise becomes highly valuable, especially in the food sector.
Context
Context is what the AI knows right now, for a specific task. Any information you share with an AI tool becomes context for that conversation or use of the tool.
Generic AI tools start with zero context about you or your business every time you open them. Effective specialized AI tools should start every task already connected to a business’ context, including its catalog, customers, and pricing, so that they can perform actions and provide information that is useful for that specific company. How well an AI tool integrates the specific knowledge of a business is what separates a tool that's generically smart from one that actually improves business outcomes.
Memory
Context is what the AI knows for a specific task. Memory is what it knows across all tasks, built up over time. Context disappears when the conversation ends. Memory persists.
Context is knowing that La Trattoria placed an order today. Memory is knowing that they order 40 cases of San Marzano tomatoes and 6 cases of Tipo 00 flour every Tuesday, that they double their order before long weekends, and what they mean when they say "the usual." Memory is what lets an AI tool get better at working with your business over time.
Learning
If training is what an AI learned before you ever used it, context is what it knows right now, and memory is what it retains over time, then learning is how all of these work together to make an AI tool actually get better at its job over time.
This doesn't mean the underlying LLM is changing. It means the system around it is accumulating knowledge. Every order it processes, every correction it receives, every customer preference it picks up feeds back into its context and memory, making it more accurate next time. When an agent processes 500 orders from the same customer and starts recognizing that "the San Daniele" always means the Levoni prosciutto, not a generic brand, that's learning. The LLM didn't change. Instead, the system got smarter about the business.
The ability for AI to learn and improve over time, including doing this automatically in well designed systems, is what makes AI such a transformational technology. But not every AI tool is designed to learn. Most chatbots don't. Building a system that genuinely improves with use takes deliberate engineering.
Hallucinations
Even though AI systems can learn, AI has no underlying understanding of what is true or false. It predicts what text seems like it should come next, and provides that answer with confidence. The problem is that something can sound completely right and still be completely wrong. While newer AI models have gotten better at expressing uncertainty, they can still present incorrect information with confidence, which is called a hallucination. Hallucinations are the core trust and risk issue with AI, and the main reason why the quality of the system built around the LLM matters so much, especially in industries where getting it wrong has real costs. Good systems will have guardrails that reduce the risk of hallucinations to a minimum.
Agents
If a chatbot answers questions, an agent actually does the work, bringing together all the concepts mentioned above. The result is you don't ask an agent "how should I process this order?" You give it the order, and it reads it, matches the products to your catalog, checks the pricing, enters it into your ERP, and flags anything it isn't sure about. That’s why it’s more useful to think of agents as something similar to a digital colleague, rather than just a tool, in that agents deliver actual outputs and do real work.
Building agents that operate reliably is a very different challenge from building a chatbot that can answer questions. To do this, agents need good prompts, the right context, excellent memory, access to tools, ways to avoid hallucinations, and the ability to learn. In an industry as complex as the food industry that involves so many moving pieces, and where access to some information can still be difficult (including the information that is still mainly in people’s heads), getting agents right is very difficult to do. But, ultimately, AI agents are where companies will see the most value in their business.
We hope this brief AI breakdown was useful. Feel free to leave any questions for us in the comments, or reach out to our team if you'd like to learn more about the specifics of using AI in the food industry at contact@getburnt.ai or on www.getburnt.ai.