Generative AI for LegalTech QA and Testing
LegalTech initially started with the digitization of legal processes. But soon, it covered all aspects of the legal domain, like handling contracts, compliance, risk management, and dispute resolution. The systems built to handle these domains became more complex than traditional legal software. Companies usually build software that can cover more. It helps them in fetching more customers, as they can cater to different kinds of services a customer might be looking for. Naturally, with the increasing complexity, the testing methods need to evolve too. Legal software is such a kind that even a minute process, like workflow, contract, or transaction, cannot be compromised. To cover such a vast domain of testing, QA methods should consist of the latest technologies that can enable testing with more efficiency and speed. This is where Generative AI becomes helpful. In this article, we will explore how GenAI is used for QA and testing in LegalTech, its benefits, and challenges.
Why QA Matters in LegalTech?
A software bug can be a source of inconvenience in most industries, but when it comes to LegalTech, it can have disastrous consequences. Any missed clause in a contract or an exposure to sensitive data can cause a severe impact on the business. When it comes to legal issues, things must be handled with utmost care. A misinterpretation of a contract or compliance issue can jeopardize the whole company’s operations and cause costly liabilities. These are not minor bugs that can just be fixed. They can severely strike the credibility of the firm.
Traditional methods of testing, which often involve human testing, usually fall short in modern LegalTech environments. Regulatory environments are often changing, which causes the LegalTech platforms to keep upgrading themselves. Relying solely on human testing slows down the process and may be prone to human error. While LegalTech platforms demand agile and faster delivery, traditional testing methods cannot simply deliver that. That’s why a modern QA strategy based on Generative AI is indispensable in such a case. It not only provides efficiency and speed but also an overall shift in the strategy that meets the modern demands of LegalTech platforms.
Generative AI in LegalTech
Generative AI in test automation cannot be more relevant in any field than LegalTech. This is especially because legal documents are quite large and intensive. Traditional testing methods require pre-written test cases that might require constant upgrades, even with the slightest change in the documents. GenAI, on the other hand, is an expert in comprehending intensive documents and generating test cases by itself. It not only creates but also adapts to the growing demand of the platform.
GenAI can read natural language input, such as contracts, regulatory guidelines, or user workflows, and can generate context-specific test assets. It can comprehend large documents that are mostly difficult and time-consuming for a manual tester, and create test cases based on that. Testing modern LegalTech platforms manually or through traditional automation systems is difficult because of scalability. These platforms cover large amounts of data and documents specific to each domain in Legal. Testing them all is only possible when a machine can read large and dense documents in seconds. That allows covering a larger scope of testing within a short time and with accuracy. And that’s what GenAI can do the best.
Applications of GenAI Testing on LegalTech
GenAI-based testing on LegalTech platforms can be best understood by looking at each area it covers.
Automatic Generation of Test Cases
Going through complex documents and writing test cases is too resource-intensive and time-consuming when done manually. GenAI can process those documents in seconds by analyzing user stories, compliance requirements, or contracts. Based on that, it can suggest the most effective test cases that can eliminate blind human spots and provide maximum coverage.
Synthetic Test Data
Arranging mock test data is difficult in traditional testing scenarios. Simply because of the privacy issue when reading a real contract or compliance document. And when synthetic data is provided, it may fall short on realism. GenAI testing platforms can generate synthetic test documents that are almost real in terms of quality and credibility, yet do not compromise the privacy of the users. These documents can be used for testing real-time scenarios for a LegalTech platform.
Validate Features
LegalTech platforms, like Legodesk, are quite rich in features. The GenAI testing platform can generate specific test cases for each of them. Features like notice generation and workflow automation can be easily tested by generating test cases through natural language commands on a GenAI testing platform. For instance, workflow automation requires several steps to be performed that include setting up the action items, linking them together, and creating a logical sequence. Through GenAI testing, these steps can be tested through multi-step prompting that generates test cases for each step.
Constant Guardianship
When software is always going through frequent updates, matching the testing speed is a challenge. But AI-powered testing allows you to keep track of those changes and adapt the test cases accordingly. Thus, maintenance becomes easier. Additionally, regression testing after code changes, a new rule, or an upgrade can be performed much faster, as it is all executed automatically.
Closing Reflection
LegalTech platforms need to be handled with utmost care due to their severity level to the business. Yet speed is also an important factor because companies need to deliver their products faster. Every upgrade or addition of a new rule or regulatory guidelines needs to be tested quickly and effectively. Any blind spots can have a disastrous impact on the business. And due to the large scale of documents and domains covered in the platform, it has simply gone out of scope for traditional testing methods. QA engineers now rely heavily on AI assistance for testing. When Generative AI can take up the task of analyzing complex and large documents by itself, it frees up a lot of time for the testers. They can use that for creating higher-level test strategies, governance, and innovation. Yet, we cannot say that AI can completely remove human dependency. Human testers are still needed to guide the system, perform exploratory tests, and create edge test cases.
