AI is creating new opportunities in every domain. ITSM is no exception to that. Generative AI has further enhanced the possibilities in ITSM and made the case for more rapid adoption of AI by ITSM teams globally. But how best can ITSM leverage the endless possibilities of prompt-based Generative AI? To understand this, one needs to understand the fundamentals of prompt engineering and be aware of common prompt engineering mistakes to avoid.
What is Prompt Engineering?
Prompt engineering is the strategic process of crafting and refining input prompts to optimize the performance of AI models, particularly large language models (LLMs) like ChatGPT. By carefully designing prompts, users can elicit more accurate, relevant, and valuable responses from the AI model, enhancing the overall quality of the interaction.
Prompt engineering, therefore, is not merely about inputting text into a model; it’s a skill that requires a deep understanding of the model’s capabilities and limitations. For ITSM professionals, prompt engineering can be particularly valuable in automating routine tasks, generating reports, and providing insights from large datasets. By effectively crafting prompts, ITSM teams can leverage the power of AI to enhance efficiency, accuracy, and decision-making.
Leveraging Prompt Engineering in ITSM
To leverage the potential of good prompt engineering in ITSM, one needs to remember a few key things. There are some best practices, which when followed, could help ITSM professionals get the desired outcomes easily and quickly.
Be clear and specific with prompts
Tell the Generative AI tool what you need in as clear and specific a manner as possible. Use precise language and reduce ambiguity. Provide a proper context – the more detailed, the better. Do not be overwhelmed by the length of the prompts. A good prompt is about 80% context and details, and only 20% of what you need. This is no hard rule, but it helps understand the importance of giving as many details and context as possible to Generative AI to get the best outputs.
Prompt structuring is crucial
Some words are considered explicit prompt instructions, such as explain, summarize, describe, list, elaborate, etc. Use these words to strengthen your prompts and help the Generative AI understand what you need. If you have specific formats or requirements in which you need the outputs, describe that as well. Again, give as much detail as possible to get more accurate outputs.
Function in a Feedback Loop mechanism
Every output from Generative AI should be used as feedback for the prompt that was entered. Based on this, future prompts should be improved to get better outputs. Keep working towards understanding how you can communicate better with the Generative AI tool, to help it help you better. You can also write another prompt with some improvements for the same requirement to check if it gives you a better output.
Keep an eye out for errors
The outputs of Generative AI tools are highly dependent on the training data and the prompts that the user enters. Generative AI, by itself, is not an expert in every subject. Instead, it builds its knowledge based on the data it gets trained on and the input it receives to derive the outcome. Even if you don’t get accurate outputs each time, keep working with Generative AI to help it understand what you are looking for. Over time, it will get sharper and more accurate. Even then, always check and double-check the outputs received from Generative AI.
A Generative AI tool is like an AI assistant. Every new assistant that gets hired needs some time to understand your requirements and how you would like specific things, how to organize your files, how to answer your calls, which calls to direct to you, and which ones to take a message to pass onto you later, etc. In the same way, Generative AI needs to explain what you need in thorough detail, and while it can still deliver outstanding results from the very first attempt, it needs to be given the details.
At the same time, be careful of what is shared with Generative AI. These tools do not have any privacy barriers so any information can and will be used to train the algorithms further and will no longer remain privileged information. So, always avoid sharing sensitive information and copyright/intellectual property information. Even if you regularly clear out the histories, the data forever stays with the Generative AI.
Things NOT to do when writing prompts
- Verifying whether the Generative AI tool being used is suitable to answer your prompts
- Writing vague and ambiguous prompts
- Writing confusing prompts with insufficient relevant information and lacking focus
- Accepting the first output received and not trying to refine the prompts more to get better outputs
- Giving no examples or details in the prompt
- Not specifying the audience, voice, tone, etc. which is information about how to structure, format, and phrase the output
- Not checking the outputs for errors and hallucinations
Following the best practices and understanding the fundamentals of prompt engineering, ITSM professionals can make their lives a lot easier. Common disciplines in ITSM can become so much more efficient and effective by embracing Generative AI.
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