The text compares writing prompts to ancient acts of communication like prayers or letters, highlighting the timeless nature of sending words into the unknown and awaiting a response. It notes that while the technology is new and the speed of return instant, the essence of the exchange remains, and how our mood influences the way we write prompts.
This study evaluates the performance of prompting strategies (chain-of-thought and zero-shot) in extended reasoning LLMs like Grok-4.1, varying the sampling temperature across 39 challenging mathematical problems. It found that zero-shot prompting peaks at moderate temperatures, while chain-of-thought performs best at temperature extremes, significantly increasing the benefit of extended reasoning.
This research study reveals that small instruction-tuned language models (LMs) using Chain-of-Thought (CoT) for arithmetic often employ a positional shortcut, copying whichever number occupies the trailing position before the answer delimiter. This shortcut dominates, even when intermediate reasoning is correct, significantly impacting answer accuracy.
Este artigo explora se os LLMs aproximam quantitativamente o significado social humano e se estratégias de prompting pragmático podem melhorar essa aproximação. Para isso, introduz métricas de calibração (ESR, CDS) e observa que os modelos reproduzem a estrutura qualitativa das inferências sociais humanas, mas diferem substancialmente em outros aspectos.
This research introduces a two-part framework for group-robust structured prediction, addressing challenges like ambiguity and heterogeneous group difficulty. It proposes a task-agnostic prompting strategy and STaR-DRO, a novel stateful robust optimization method for learning concentrated on persistently hard groups.
This article provides 10 tips and tricks for improving AI video generation with Kling 3.5, based on generating hundreds of clips. The key recommendation is to use full scene descriptions instead of short subject labels for better output quality and fewer artifacts.
Gemini 3.1 Flash TTS is a new text-to-speech model allowing users to achieve precise audio performance through prompting. This article offers tips on guiding the model, using context such as audio profiles, scene descriptions, and tags to control delivery.
O artigo discute por que as IAs frequentemente produzem respostas genéricas e sem vida, argumentando que o problema não é do modelo, mas sim da forma como os prompts são elaborados, geralmente de maneira neutra e superficial. Essa abordagem leva a IA a gerar respostas que são a média estatística dos padrões de treinamento, resultando em conteúdo sem impacto.
This post explains how to use Amazon Nova 2 Lite for content moderation through structured and free-form prompting techniques. It also benchmarks the model's capabilities against several foundation models using public datasets, grounded in the MLCommons AILuminate Assessment Standard.
This paper introduces Derivation Prompting, a novel prompting technique for the Retrieval-Augmented Generation (RAG) framework. The method aims to reduce hallucinations and erroneous reasoning in Large Language Models (LLMs) by systematically applying predefined rules to derive conclusions. A case study demonstrated a significant reduction in unacceptable answers compared to traditional RAG methods.
This content features 50 practical and underrated tips for advanced users of Claude AI. It covers topics from coding and collaboration to prompt hacks and hidden features to optimize tool usage and context management.
It is crucial to maintain the focus of the conversation when interacting with an LLM to ensure coherent responses. Avoiding abrupt topic changes helps optimize the model's performance.