However, LLMs should be considered “idiot-savants” because they have extensive knowledge but lack common sense and can be overconfident when they don’t know the correct answer. Unlike Google, which provides hard-coded answers or a list of links based on keywords, LLMs can generate human-like text responses/paragraphs in response to any question or instruction. GPTs are a subset of large language models (LLMs) that, like Google, have crawled most of the text on the internet. However, for those interested, a quick introduction to testing AI systems is provided here. This article focuses on testing new GPT-based features and apps, rather than discussing the nuances of testing the core GPT systems themselves, which are built by engineers with billions of dollars at their disposal. By being aware of these factors and implementing best practices, developers and testers can ensure that their GPT-based apps generate accurate and reliable output. ![]() ![]() This article introduces a range of testing techniques specific to GPT-based apps, including Bias, Drift, Performance, Privacy, Security, Versioning, Prompts, , Response Parsing, etc. As more and more individuals and businesses rush to build GPT-based apps, it’s crucial to know how to test them. Unlike traditional software design and testing, which prioritize highly-structured and predictable output, GPT is designed to offer incredible flexibility in natural language input and output. ![]() With the rise of GPT-based apps, developers and testers must consider new challenges when measuring and ensuring their products' quality. The emergence of ChatGPT is quickly transforming software development, but the required software testing strategies are lagging.
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