This month we take up our colleague Heinke's topic suggestion and learn everything worth knowing about prompting.
Two and a half years after ChatGPT's release, the hype around chatbots has not died down, and by now almost everyone has experimented with one. While chatbots are already in use at many companies, others have not yet seen the benefit or were not satisfied with their test results. What might be behind that? One cause of unsatisfying answers can be inadequate prompts. So that you get the best possible results in future, we have put together an overview of what to keep in mind when prompting.
What is prompting?
A prompt is the input given to the language model, or chatbot, in order to obtain a specific answer or a particular behaviour. For many people prompting has developed into something of a sport, which serves not only to achieve the best possible results but can also be used abusively. Some people use prompting to try to produce faulty results deliberately, for instance to damage the companies providing the service.
Why does prompting matter?
Prompting matters because it influences the quality and relevance of a language model's answers. A well-worded prompt can help the model deliver exactly the information needed while avoiding unnecessary or irrelevant material. That saves time and makes working with language models more efficient.
Tips for effective prompting
- Be clear and precise:
A good prompt should be clear and precise. Avoid vague or ambiguous wording. Give the model as much context as possible to get the answer you want. - Ask specific questions:
Ask specific questions rather than making general requests. For example: “What steps are necessary to start a new project?” rather than “Tell me something about project management.” - Use examples:
Give the model examples to illustrate the answer you want. For example: “Write an email thanking a customer for their order. Here is an example: ‘Thank you for your order. We look forward to delivering your goods soon.’” - Use instructions:
Give the model clear instructions about what it should do. For example: “Write a blog post about the benefits of artificial intelligence. The post should be at least 500 words long and written in an informative tone.” - Avoid ambiguity:
Avoid ambiguity and unnecessary information. A clear, precise prompt helps the model deliver the answer you want faster and more accurately.
Examples of effective prompting
- Answering questions:
- Prompt: “Explain the basics of artificial intelligence to me.”
- Answer: “Artificial intelligence (AI) is a field of computer science concerned with developing systems capable of performing tasks that normally require human intelligence.”
- Producing text:
- Prompt: “Write a blog post about the benefits of artificial intelligence. The post should be at least 500 words long and written in an informative tone.”
- Answer: “Artificial intelligence (AI) has made enormous progress in recent years and offers a wide range of benefits across industries. From automating routine tasks to improving decision-making – AI has the potential to increase efficiency and productivity considerably in many areas…”
- Translations:
- Prompt: “Translate the following text into German: ‘The quick brown fox jumps over the lazy dog.’”
- Answer: “Der schnelle braune Fuchs springt über den faulen Hund.”
In conclusion
Prompting is a powerful tool that helps you get the best results out of language models. With the right techniques and tips you can make sure your prompts are clear, precise and effective. Use these strategies to improve the accuracy and relevance of the answers and to make your work more efficient.
