This post discusses a pattern to prevent a class of prompt injection attacks in LLM-based solutions. It emphasizes the importance of building strong foundational patterns to mitigate risks and avoid potential pitfalls. By implementing this pattern, teams can enhance the security of their tool-based solutions.
We built a bot with Microsoft Bot Framework (MBF) to tap into a multitude of channels, including a client's existing Android app, and implemented localization features, to connect with potential customers on their preferred online platforms.
When considering bots, it’s often important for organizations to have the ability to “hand off” a customer from a bot to a human agent seamlessly. We implemented an unopinionated e-2-e solution called Handoff for bot authors to implement a variety of scenarios, using the Microsoft Bot Framework Node.js SDK.
Microsoft partnered with MaaS Global to build a bot using the Microsoft Bot Framework Node.js SDK that can be deployed with the Serverless framework.
This post outlines the steps of creating a bot that interacts with a user to collect missing LUIS entity data before processing their request.
Adding unit testing for conversational flow to Microsoft Bot Framework projects, including ones using LUIS.
A wrapper for the Microsoft LUIS Cognitive Service that provides universal language support (after training) using the Cognitive Service Translation API.
Creating a configuration-based bot service that runs on Azure and supports multiple customers' applications using Microsoft Bot Framework.
How to provide a backend service for a QnA bot in languages not currently supported by QnA Maker by utilizing Azure Search.
Integrating payment service providers with the Microsoft Bot Framework.
