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23 mar 2019 · Output validation is so important because if your users can’t trust your calculations, they won’t use your application! Remember to always begin with thinking about what you should test, and then design automation that verifies the correct functionality even in boundary cases.
Input validation is performed to ensure only properly formed data is entering the workflow in an information system, preventing malformed data from persisting in the database and triggering malfunction of various downstream components.
15 gru 2021 · The solution: model validation. Validation uses your model to predict the output in situations outside your training data, and calculates the same statistical measures of fit on those results. This means you need to divide your data set into two different data files.
A guide to quantitative and qualitative output validation for Retrieval Augmented Generation
6 wrz 2023 · A Dive into Pydantic Modeling. Utilizing Pydantic models, let’s craft a structured output for our data. We define a CityResponse model that captures key details - city name, country it's in,...
Insufficient input/output validation exposes our application to critical attack vectors, including SQL injection, XSS, command injection and path traversal. These vulnerabilities can lead to unauthorized access, data manipulation, code execution, and compromise of the entire backend system.
