Policies and procedures are essential documents for any organization, as they define and guide its operations and activities. However, writing these documents can be challenging and time-consuming, especially for complex or technical topics, such as user acceptance testing (UAT).
UAT is a type of testing that involves the end users or clients of a software system or application to verify if it meets their requirements and expectations. UAT is usually the final testing stage before the software is released or deployed, and it requires careful planning, execution, and documentation.
In the past, companies would hire expensive specialist consultants who have the expertise and experience in this field to help them write complex policies and procedures. However, with the advancement of artificial intelligence (AI) and natural language processing (NLP), there is a new option available for companies: large language models (LLMs).
What are LLMs?
LLMs are a type of generative AI that can produce text based on a given input or prompt. LLMs are trained on massive amounts of text data to learn the patterns, structures, and relationships in natural language. LLMs can perform various language-related tasks, such as translations, summaries, questions, answers, sentiment analysis, and more.
Some examples of LLMs are OpenAI’s GPT-4, Google’s BERT, and Facebook’s BART. These models have shown impressive capabilities in generating coherent, fluent, and relevant text for various purposes and domains.
How can LLMs help with writing policies and procedures?
LLMs can help companies with writing policies and procedures for UAT by taking some of the grunt work out of it and enabling internal staff to just edit or curate the results. Here are some of the benefits of using LLMs for this task:
- LLMs can save time and money by reducing the need for hiring external consultants or spending hours on research and writing.
- LLMs can generate text based on keywords, topics, or examples provided by the user, making it easier to customize the content according to the specific needs and preferences of the company.
- LLMs can produce text in a clear, concise, and consistent manner, following the best practices and formats for writing policies and procedures.
- LLMs can provide suggestions, alternatives, or variations for different scenarios or situations that may arise during UAT.
- LLMs can incorporate relevant information from various sources, such as industry standards, regulations, best practices, or previous projects.
How to use LLMs for writing policies and procedures?
Using LLMs for writing policies and procedures is not as simple as typing a query and getting a ready-made document. LLMs are powerful tools that require careful guidance, supervision, and evaluation by human experts. Here are some steps and tips on how to use LLMs for this purpose:
- Define the scope and purpose of the policy or procedure you want to write. Identify the target audience, the objectives, the criteria, and the expected outcomes.
- Choose an appropriate LLM that suits your needs. Consider factors such as the domain, the language, the quality, and the availability of the model.
- Provide a clear and specific input or prompt to the LLM. You can use keywords, topics, examples, questions, or instructions to guide the model on what you want it to generate.
- Review and edit the output of the LLM. Check for accuracy, relevance, completeness, and coherence of the text. Correct any errors, gaps, or inconsistencies in the content, style, tone, or structure.
- Validate and verify the policy or procedure with the stakeholders, such as the end users, clients, managers, or regulators. Get feedback and approval before publishing or implementing the document.
Writing technical policies and procedures can be a daunting and tedious task, but with the help of LLMs, it can be made easier and faster.
Note: we generated the above text using BingChat, which runs a version of ChatGPT. We generated the image above using BlueWillow, an AI image generator.
Note 2: Click here for an example of an AML policy for a small UK investment manager written by an LLM