Large language models (LLMs) rely on a set of parameters that directly influence how text is generated — affecting randomness, repetition, length, and coherence. Understanding of these parameters is essential when working with LLMs in research, application development, or evaluation settings. While chat-based interfaces such as ChatGPT, Copilot, or Gemini typically
Navigating Horizon Europe’s 2025 Work Programme means dealing with twelve separate documents, each several hundred pages long. These include funding calls, eligibility conditions, strategic priorities, and legal annexes—making it difficult to locate critical information quickly. To address this challenge, poltextLAB developed a domain-specific Custom GPT (Horizon Navigator '
In our previous blog post, we examined how useful publicly available Custom GPTs are for academic tasks. While many showed promise, especially for brainstorming or outlining, they often fell short in areas where accuracy, source verification, and methodological transparency are crucial. In short: if your work demands high standards of
As ChatGPT is increasingly used in academic research, understanding how to manage privacy, control data usage, and make informed choices about sharing outputs has become essential. While our previous blog post focused on configuring the ChatGPT interface—through settings such as personalisation, memory, and model selection—this follow-up explores tools
As GenAI tools become increasingly embedded in academic workflows, researchers seek ways to tailor these systems to their specific needs. OpenAI’s ChatGPT interface offers a number of personalisation options and privacy-focused features that can significantly enhance research efficiency, consistency, and ethical compliance. This blog post introduces the key settings
