Systems | Development | Analytics | API | Testing



Private AI vs Public AI: 4 Key Facts

Artificial intelligence (AI) has reached a tipping point in the public consciousness. Much of this has been driven by technology developments related to large language models (LLMs) and the release of generative AI tools, including ChatGPT from OpenAI. However, for enterprises shaping forward-looking AI strategy, a critical part of the conversation that needs to be addressed is the issue of private AI vs. public AI.


How is ChatGPT helping the Payments Industry

Chat Generative Pre-Trained Transformer (ChatGPT) is a pre-trained AI bot that can chat with you and generate relevant natural language text in response to your input text. The most impressive aspect of this software is that it promptly provides clear and concise information. Financial institutions, payment gateways, and processors securely process payment transactions.


Generative AI in Software Testing

Generative AI in software testing is an advanced approach that augments human testers to make the testing process faster and more efficient while improving the quality of the software test results. At Testlio, we use artificial intelligence in software testing to assist expert QA testing managers in new test creation, faster test case refactoring, and creating issue reports with significantly fewer errors. In essence, generative AI helps enhance human performance.


10 Best AI Testing Tools for Test Automation

Over the years, software developers have heavily relied on manual testing processes, which continue to play a vital role in the testing phase. However, it’s important to acknowledge that there are scenarios where test automation becomes important too. That is where test automation tools come in handy. In recent years, automated testing has emerged as a valuable addition to the field of software testing.

LLM ChatBot Augmented with Enterprise Data

This video demonstrates how to use an open source pre-trained instruction-following LLM (Large Language Model) to build a ChatBot-like web application. The responses of the LLM are enhanced by giving it context from an internal knowledge base. This context is retrieved by using an open source Vector Database to do semantic search.