How LLMs work
You don't need any math to use AI tools well, but you do need a working picture of what they are doing. This module gives you that picture, and shows why the same tool can be useful and wrong in the same answer.
Where you already meet AI¶
AI already shows up across veterinary practice: reads on radiographs, flags from lab analysers, and scribes that write up the notes from a consult. This course is about the chat tools, such as Claude, ChatGPT and Gemini. They are built on large language models, or LLMs.
How an LLM answers¶
An LLM writes its answer a small piece at a time. At each step it predicts a likely next word, based on patterns in the very large amount of text it learned from. It is not looking anything up unless a search tool is switched on.
The diagram above shows the machinery at toy scale. A signal passes forward through layers of connections, and each connection makes it stronger or weaker. Training sets the strength of every connection. Nothing in that process checks whether an answer is true: the model learns what reads as likely.
What follows from that¶
- It has a training cutoff. It can miss recent guidelines and new drugs.
- It can be fluent, confident and wrong. The usual name for this is hallucination.
- It can give different answers to the same question on different days.
- It tends to agree with whoever is asking. A leading question tends to get the answer it leads to.
- It does not remember you between chats, unless a memory feature is switched on.
Why it is still useful¶
It is very good at language work: drafting, summarising, rewording, explaining, and putting messy text into order. Those jobs are where the course spends most of its time.
Try it yourself¶
Open any AI chat tool you have access to. Each test takes a couple of minutes.
- Ask the same clinical question twice, in two new chats. Compare the answers. Where do they differ?
- Ask about something recent, such as a guideline or a drug from the last few months. Does the tool tell you it may not know?
- Ask a leading question, such as "Isn't drug X the best choice here?" Then ask it neutrally: "What are the options, and what are the trade-offs?" Compare the two answers.
Keep what you notice. Module 4 comes back to these habits and turns them into checks you run every time.
The one thing to take away¶
Fluent is not the same as right. The rest of the course is practice at getting the benefit while you stay the one in charge.
Where next
Using AI in practice: Module 2: The landscape, and which tool for what (coming soon)
Building a RAG system: Module 0: Setup and notebook primer (coming soon)
Questions will go to a shared channel where each answer is written once and shared with everyone taking the course. The channel opens at launch.