In many product workflows, templatic documents such as receipts, bills, and insurance quotes play an important role. Currently, these documents are processed manually, which leads to a high level of inefficiency and errors.
AI Documentation can help automate these processes. However, creating AI documentation requires extensive knowledge of your software’s behavior and the problems it solves.
Overview of AI Documentation
AI Documentation is the process of creating and delivering product documentation using AI-powered tools. These tools use natural language generation (NLG) and machine learning algorithms to automatically generate and structure documents such as user stories or technical reports.
These tools can help Product Managers (PMs) generate ideas, organize their thoughts, and improve the quality of their writing. But in order to create meaningful product documents, AI tools need input that includes the specifics of your software’s behavior, the problems it solves, and the information you learned from user interviews and research.
Despite the huge potential of AI, there are a lot of challenges when it comes to documenting it. One key challenge is that AI has a black-box character, making it difficult to understand and verify what the technology is doing.
Creating AI Documentation
Businesses all over the world rely on documents to store and convey information. However, these documents often need to be digitized for them to become useful, which can require time-consuming and manual processes.
Using AI to automate these manual processes is a great way to reduce data entry errors and streamline document processing. But it’s also important to feed AI quality input so that it can produce meaningful documentation.
The current state of AI documentation is unclear. Interviewees point to existing privacy laws and a lack of guidance on how to document AI.
The interviews also showed that there is a trade-off between sharing data documentation and the protection of intellectual property. In particular, the risk level of AI use cases plays a key role in determining how and when to document AI.
Using AI Documentation
Product documentation needs to contain a high level of detail about your software’s behavior, the problems your product is trying to solve, and the information you learned in user interviews and research. Document AI can help you write your docs more quickly and efficiently.
However, the quality of the input is critical to AI’s ability to produce meaningful documents. Without proper input, AI is more likely to produce factual errors such as inaccurate or erratic numbers and fabricated product information.
The second group of AI documentation beneficiaries, the relaxed adopters, apply AI in use cases with a low level of risk. They use AI documentation in a hands-on way primarily to satisfy technical and team coordination needs.
Documentation of AI applications is crucial to ensure fairness, accountability, and transparency. However, existing guidelines for software documentation are not suitable for the AI environment.
The interviewees in this study come from different industries and have different backgrounds, but there is a common thread between all groups: the need for high-quality documentation.
In the first group, the excited adopters, AI is applied in use cases with a high degree of criticality, and it’s likely that their applications will be audited. For this reason, they are aware of the need for a robust documentation process.
They document their AI applications in a hands-on manner to satisfy both technical and team coordination needs. This approach enables their development team to remain innovative while also protecting company-internal knowledge and ensuring that their AI is documented sufficiently.