Prompt engineering is used in artificial intelligence to refine inputs (prompts) to get the most accurate model outputs. In this study, the concept of chain of thought prompting is introduced which elicits reasoning in LLMs.The paper argues that prompting models to generate intermediate reasoning steps significantly boosts their ability to accurately solve multistep problems like arithmetic, common sense and symbolic reasoning.
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As you go deeper down the rabbit hole building LLM-based applications, you may find that you need to root your LLM responses in your source data. Fine
Fine-tuning Large Language Models (LLMs) has revolutionized Natural Language Processing (NLP), offering unprecedented capabilities in tasks like language translation, sentiment analysis, and text generation.
Fine-tuning is the process of adjusting the parameters of a pre-trained large language model to unlock the full potential of LLMs in specific domains or applications.
Swift packages are a neat and simple way to bundle up and share code. They remove the overall complexity by not requiring an Xcode project but instead
The HotSignal class is a custom wrapper designed to expose a sequence of events using Swift’s Async/Await features.
Today we will be exploring the Mutex struct, seeing how it can be used to prevent crashes and undefined behaviour due to simultaneous reads & writes to shared mutable data.