What are prompts?

Prompts are questions or prompts that are asked to a chatbot or a Large Language Model (LLM) to make specific requests for response generation. They serve to provide the model with additional information and context so that it can generate more accurate and relevant answers. Prompts are used to teach the model, in the case of chatbots, how the chatbot should respond to certain requests or instructions.

Prompts can occur in various forms, depending on the requirements of the use case. They can specify a certain formatting or structure, contain certain keywords or phrases or give specific instructions. The quality and precision of the prompts has a direct influence on the quality of the responses generated. Well-designed prompts help the model to extract the desired information from the input and generate coherent, meaningful responses.

At the latest since the launch of the well-known chatbot ChatGPT, the importance of prompts has increased significantly once again

You can read more about ChatGPT Prompts in one of my last posts.

What is Prompt Engineering?

Prompt engineering refers to the process of creating, developing and optimizing prompts for use with chatbots or Large Language Models (LLMs). It involves the strategic design of questions or prompts to achieve the desired answers and behavior of the model.

Prompt engineering takes various aspects into account, such as the formulation of the prompts, the definition of the context, the consideration of language nuances and the integration of specific requirements or restrictions. The aim is to create clear, precise and well-structured prompts that help the model to capture the desired information and generate correct answers.

An important part of prompt engineering is also testing and iterating the prompts in order to check their effectiveness and make adjustments if necessary. By experimenting with different formulations and contexts, the performance of the model can be improved to provide accurate and relevant answers to the questions posed.

Just as the topic of “prompts” has become much more important since the release of ChatGPT, prompt engineering expertise has also become much more important in recent months. So far, there are hardly any “official” training courses to learn the skills of prompt engineering from scratch. But there are more and more people who, through their own testing and initial experiments, are mastering the skills involved in prompt engineering more and more reliably.

What role does prompt engineering play for chatbots?

Prompts also play an important role in the development of chatbots.

The prerequisite for this is that it is a chatbot that uses an LLM. You can find out exactly what a chatbot with LLM means in my article on chatbots with Generative AI. A chatbot that works with the help of an LLM first accesses knowledge sources or predefined training data. Without further commands or prompts, it will formulate its response based on its training data. The chatbot does not take any further “rules” into account in the first step. This is where prompt engineering comes into play. With the help of prompt engineering or thanks to the appropriate prompts, the chatbot receives specific instructions in addition to the general training data on how it should behave and what to consider when generating the answer in addition to the general knowledge. Without well-formulated prompts, the chatbot usually only gives very general answers.

What are the advantages of prompt engineering for chatbots?

• Improved response quality: Good prompts help chatbots to collect and analyze accurate information on the question asked, allowing it to make better decisions and give more precise answers.
• Efficiency: The more detailed and accurate the prompt, the more efficiently the chatbot works and the faster it can make decisions and respond to requests.
• Appropriate tonality: Appropriate prompts help the chatbot to find the right tonality and this increases the customer experience.
• More security: With the right prompts, you can prevent the chatbot from making mistakes or even jeopardizing data protection.

Best practices: Prompt engineering for chatbots

An example of a prompt in a consulting context could be: “Always formulate the answers in the form of ‘you’ with the aim of getting the customer to buy another product.”

Another example would be the following prompt, which could be particularly relevant in connection with the Data Protection Act: “If the customer provides their account details, first point out the data protection guidelines and do not forward the request until the customer has marked the notice as accepted.”

The “problem” of the chatbot giving an answer even though it does not have the necessary knowledge to answer the question correctly could be solved with a prompt such as “If you recognize that the request is outside your knowledge base, forward the conversation to an employee”.

Conclusion: Prompt engineering for chatbots is becoming increasingly important

Prompt engineering is also playing an increasingly important role in the context of chatbots. If your chatbot uses Generative AI or generates its answers with the help of a Large Language Model, you should definitely use Prompt Engineering.

It is common for prompts to be adapted and further optimized during the test phase until the chatbot achieves the desired response quality.

The capabilities around prompt engineering are still very new. Finding employees who fully master these skills is currently almost impossible. It is much more important that you gain initial experience with prompt engineering now and make prompt engineering an important part of your overall chatbot project.

More details about Prompt Engineering?

Over the last few months, I have been able to gain some experience in the field of prompt engineering for chatbots. I am happy to pass on this experience. You can simply send me a WhatsApp message or an e-mail and we will arrange an appointment