New series: from zero-shot prompts to RAG - How YOU can make better use of LLMs
Prompting depends on good and clear wording and an understanding of natural language. Nevertheless, certain techniques and methods can significantly improve the result of a prompt.
The term "prompt engineering" is being used more and more frequently. More and more people are calling themselves prompt engineers. This refers to people who can demonstrate a certain knowledge of writing prompts for LLMs. Personally, I don't think much of this title ...
The bottom line is that prompting depends on good and clear wording and an understanding of natural language. Both things that most of us already have. Nevertheless, it's fair to say that certain techniques and methods can significantly improve the outcome of a prompt.
In the following weeks, I will shed more light on these techniques and methods. In a series every Monday, we will look at the following techniques in detail:
- Zero-Shot-Prompting
- Few-Shot-Prompting
- Chain-of-Thought-Prompting
- Meta-Prompting
- Prompt Chaining
- Self-Consistency-Prompting
Let's start with the easiest kind: zero-shot prompting.
Zero-shot refers to the method that we probably use most of the time anyway. In simple terms, this method is nothing more than a question and an answer.
Prompt > Answer
It is a method in which an AI language model is asked to perform a task without being given a concrete example beforehand. This means that the task is described solely by a clear instruction - the model must understand what is meant from the context and its general knowledge and react accordingly. A typical example would be to simply say: "Translate this sentence into English: 'I'm hungry'." - without first showing what such a translation should look like. The term "zero-shot" is derived from the idea that zero examples or training shots are given. Zero-shot prompting is particularly useful if you want to work with a model quickly and without much preparation. It shows how well a model can generalize tasks and solve them without specific training.
However, this technique is rather unsuitable for complex problems. This is where the other methods come into play, which we will look at in future articles.
