Basic things (and some vocabulary) to know when working with LLM tools.
1. The model will lie to you
When a LLM model doesn’t know the answer to your question, it can –and will– straight up lie to you. You can design elaborate prompts to try to prevent this but it will still happen.
2. Most LLM tools don’t know how to browse the Web (without special plugins)
One of the most common things that trip people up when using LLM tools is that they will convincingly seem to summarize a Web page or pull data from an URL — even though they usually can’t browse the Web. In other words, most answers based on URLs you give them will be 100% made up. If the tool doesn’t have a plugin that is specifically designed to read URLs, assume the model can’t browse the Web.
3. When sharing an answer (or a screenshot), always include the full prompt
Sharing interesting answers is usually useful only if you remember to share the full query and prompt as well. If you share a screenshot, make sure the full question (and the prompt if any) is displayed as well so that the answer can be replicated.
4. Don’t ask LLMs to do calculations
As the name implies, LLMs are based on (natural) languages. As powerful they are with language, most are absolutely terrible with algebra. Do not try to do maths with LLM tools, and if you for some reason need to, force the model to show as many intermediate steps as possible.
5. Instead of a long and complex query, use several shorter ones
Splitting a complex task into several steps usually yields much better results. In general, if you force the model to work with steps, you can guide it easier and hence get much better results.
6. The output of the tool is fully based on the data it was trained on
All LLM models are trained on a known dataset and they literally don’t know anything that is not in that dataset. (They might hallucinate answers, though!)
Related issue is the knowledge cutoff. For example ChatGPT knowledge cutoff is September 2021, it does not know anything that has happened after that date.
7. Mastering these tools takes time and effort
Like any complex tools, LLMs take lots of practice to master. A whole new field of expertise called Prompt Engineering has been born from the experimenting and learnings of the early pioneers working on these tools. Some speculate that in the near future talented prompt engineers are as sought after as talented developers are today.
If you want to learn and keep up, you need to put the time in and start learning.
8. Learn the basic vocabulary
In order to be able to communicate with others and to learn more, you need to know at least the very basic vocabulary. Here’s some basics terms to get you started:
- AI (Artifical Intelligence) is an umbrella term meaning everything from Siri to ChatGPT. (Think “motorised transport”)
- LLM (Large Language Model) is a type of a machine learning algorithm for solving problems using computational linguistics and probabilities. (Think “a car” vs a train or a motorcycle)
- ChatGPT is a prorietary tool by OpenAI, first published in November 2022. (Think “Porsche 911 GT”)
More advanced terms:
- The trained LLM algorithms are called models. Some known ones are for example GPT-3, GPT-4 and Claude.
- Prompts are the initial instructions sent to the LLM model when you query it. They can be complex and lengthy, or something very simple like “You are a helpful assistant”. (See Awesome ChatGPT Prompts.)
- When the LLM model makes stuff up and lies to you, it’s called a hallucination. All models hallucinate, and often they can be very convincing.
- Temperature is a feature of an LLM model that describes in layman’s terms how “creative” the output is. The scale goes from 0 (no creativity) to 1 (very much creativity).