Prompt engineering

When is prompt engineering used?

Prompt engineering is used in several scenarios where AI language models are employed. Prompt engineering is flexible and it can be applied to almost all of the domains out there. It is done when there is a need to improve the output quality, enhance the user experience and guide the AI model to meet the user’s expectations.

Where is prompt engineering used?

Prompt engineering can be used in various ways that include the presence of language models. Here are a few of the scenarios where prompt engineering is normally utilized:

• Content Making: Language models are used to generate many articles, stories, or other forms of digital and practical content. Prompt engineering can be used to train the model into producing relevant and useful results.
• Chatbot Development: Prompt engineering can be used to create conversational bots or chatbots that are used in almost all websites these days to guide the website visitor with the webpage and help the visitor understand the organization’s agenda.
• Information Retrieval: Prompt engineering can be used to gain insights into how one can make things work in any of the desired fields of work. Language models can be of great help in letting the prompter delve deeper into any aspect of the subject, provided the prompter is skilled at elaborated and subject-specific prompting.
• Code Generation: Writing code is one of the most complex and complicated tasks that involve the need for prior knowledge of logic and syntaxes. With prompt engineering, one can make the AI model code for them in a fraction of a second with the right and concise prompts.

Who uses prompt engineering?

Anyone and everyone can use prompt engineering but there are specific sectors of people who benefit hugely from the prompt engineering methodologies. They are:

• Chatbot Developers: Chatbot developers need to master prompt engineering. When they build a chatbot they have to feed the chatbot with certain prompts that will elicit a specific functionality and conversational tone out of the chatbot that helps in response building.
• Data Scientists and ML engineers: Data scientists and ML engineers are responsible for training and fine-tuning language models. Using prompt engineering can help improve the language model’s performance and behaviour. They are responsible for matching the language model’s responses match with the user’s prompts. Hence prompt engineering is very essential for them.
• Content generation specialists: Content generation specialists use prompt engineering to produce content that meets the requirements in terms of subject, length, style, quality, and relevance of the content.
• Natural Language Processing Researchers: NLP Researches research on how prompt engineering will elicit variations of responses from the AI model and how it impacts the way the AI model functions and thinks. The NLP researchers analyze the way different prompts affect AI models and try to alter the prompts for optimal results.

Why prompt engineering is used?

Prompt engineering can be used for various reasons; however, the most important ones are:

• Control: AI models like ChatGPT and Google Bard can create responses that are varied but it is always not the most accurate. To bring control in the accuracy of AI model responses and to train the AI model to be specific and concise, prompt engineering is necessary.
• Eliminating biased responses: Querying with language models can sometimes result in a lot of biased information that will not provide the actual perspective around the subject. By using engineered prompts one can eliminate the chances of a biased response.
• Task Optimization: By engineering prompts it is possible to assign AI models, specific tasks and roles it should fulfil which results in the enhancement of the model’s ability to give user-specific results.
• Quality Improvement: Vague prompts generate vague generic results from an AI language model that is not always very helpful. By giving in engineered prompts, one can elevate the quality of the responses.

How to engineer prompt?

Here are some points to remember before prompting an AI model:

• Clarity: Clarity is important for effective communication. One should clearly describe what they want to seek from the AI model. So, maintaining clarity in a prompt may help.

Eg. I am a data scientist and I have a cookies and milk dataset which contains the amount of milk used for each brand of cookies all over Australia. Suggest me five Python visualisation functions with its syntax to visualize my data best.

• Controlling Response Length: AI models can be quite elaborate in their answers which will not only increase the reading time but also keep the prompter from the AI model’s ability to keep it crisp.

Eg. Elaborate on the unsupervised learning algorithms used in machine learning with examples under 1000 words.

• Iterate through the prompts: Prompt engineering is an iterative process and the real refinement of the large content that AI models can be boiled down to what one really requires by providing necessary prompts one after the other.

Eg: Prompt 1- Suggest free online platforms where I can code Python for data analysis.

Prompt 2- Suggest me free online platforms where I can code Python for data analysis for Windows Operating System.

With these three steps, prompting can be made easy.

How many types of prompt engineering is there?

Well, there are several prompt engineering methods, and more to come. According to our research, we have many different ways of prompt engineering. They are:

• Giving AI model a role: You can give AI model any role of your choice to get maximum information about the particular field. AI models can even code for you, if you give the right prompts to it.

Eg. Prompt: You are an expert in python and you should generate the most optimal code for converting a word document to pdf.

• Letting the AI model ask questions: It is important to let AI models ask questions as it allows us to reflect on things we did not contemplate before.

Eg. Prompt: You are an expert in python and you should generate the most optimal code for converting a word document to pdf. Ask me questions related to it before answering.

To this question the AI model has asked questions such as “Should I use third party libraries?”, “What operating system should the program support”

These are some questions one wouldn’t think of before. So by prompting and letting the AI ask more questions one can achieve precise responses from the AI model.

• Giving experience to the AI model: AI model will fit into whatever mode one fits it into. Giving the AI model experience would let the prompters know about the intricacies and dept of information an AI model contains.

Eg. Prompt: You are an expert in the field of machine learning and you have 15 years of experience in the field. What are the courses one should take as a beginner in ML to transition their career path into an ML engineer?