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  • LLM-based Text Summarization: Novice to Maestro - GitHub
    LLM-based Text Summarization: Novice to Maestro 🚀 A comprehensive guide and codebase for text summarization harnessing the capabilities of Large Language Models (LLMs) Delve deep into techniques, from chunking to clustering, and maximize the potential of LLMs like GPT-3 5 and GPT-4
  • Generate AI practice tests - Quizlet
    Instantly turn your notes into practice tests with Quizlet's AI test maker Create personalized tests, including multiple-choice questions, to check your understanding, identify focus areas, and feel fully prepared for test day!
  • Mastering Chain of Thought (CoT) Prompting for Practical AI Tasks
    One technique that consistently stands out is Chain of Thought (CoT) prompting This approach pushes the model to “think aloud,” making reasoning processes transparent, structured, and highly effective for solving complex tasks
  • Comparing LLMs for Text Summarization and Question Answering
    Learn how to select and optimize models based on task-specific requirements like computational efficiency and result quality Explore practical implementations of text summarization using BART and T5, and question answering with BERT and DistilBERT
  • Chain-of-Thought (CoT) Capabilities in OpenAIs o1 models
    Chain-of-thought (CoT) reasoning allows models to break tasks down into logical steps rather than simply providing a direct answer, which opens the door for more nuanced problem-solving Two models—O1 Mini and Preview—are pushing the boundaries of Chain-of-Thought (CoT) reasoning, each introduced by OpenAI with specific use cases in mind
  • Use a generative AI foundation model for summarization and question . . .
    In this post, we demonstrate how to construct a real-time user interface to let business users process a PDF document of arbitrary length Once the file is processed, you can summarize the document or ask questions about the content The sample solution described in this post is available on GitHub
  • Chain of Thought Prompting (CoT) - humanloop. com
    Learn what chain-of-thought prompting is, how it improves large language model (LLM) reasoning, the different types of chain-of-thought prompting and other benefits and limitations
  • Highly Efficient Prompt for Summarizing — GPT-4 : r ChatGPTPro - Reddit
    The first prompt works on smaller models, but you may as well forget the JSON thing, and even have to intervene on the fly, triggering different sections of the summary with the numbers or points of your chosen outline ToC of summary
  • A Guide on Chain-of-Thought (CoT) Prompting - F22 Labs
    CoT involves guiding the model to think step-by-step, much like how humans approach multi-step problems This blog will explore the key differences between standard prompting and CoT, dive into different types of CoT prompting, and weigh the advantages and disadvantages of this innovative technique Standard Prompting vs Chain-of-Thought Prompting
  • 7 Chain of Thought Techniques to Optimize AI Performance
    In this post, we’ll break down 7 different types of chain-of-thought prompting, and explain what each does, how and why it works, and which you should use for your use case Constrained Chain-of-thought





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