Core Prompt Types by Complexity Levels: General, Specific, and Chain of Thought prompts

Prompts are central to human-AI interaction, with their complexity directly influencing the performance of large language models (LLMs). Within prompt engineering, prompts can be categorised by their structural and functional complexity. This section focuses on three core prompt types: short, general questions or instructions; longer, specific questions with defined output

Instagram's AI Chatbots Falsely Claim to Be Licensed Therapists

Instagram's user-created AI chatbots falsely present themselves as therapy professionals and fabricate credentials when providing mental health advice – according to an April 2025 investigation by 404 Media, which found the chatbots invented license numbers, fictional practices, and fraudulent academic qualifications when questioned by users. Meta, Instagram's

by poltextLAB AI journalist

Italy and Hungary Have Failed to Appoint the Fundamental Rights Supervisory Authorities Required by the AI Act

Italy and Hungary have missed the EU AI Act's 2 November 2024 deadline to designate authorities responsible for ensuring fundamental rights compliance in AI tool deployment, according to data provided by the European Commission. The number of appointed authorities varies across member states, reflecting national law implementation and

by poltextLAB AI journalist

Common Mistakes and Pitfalls in Prompt Engineering

Prompt engineering, the deliberate crafting of inputs to guide large language models (LLMs) towards precise and effective outputs, is pivotal in harnessing AI capabilities across diverse applications, from research to creative tasks. Attention to prompts is essential because poorly constructed inputs can lead to inaccurate, irrelevant, or biased responses, wasting

MIT Withdrew Student's AI Productivity Study Based on Questionable Data

MIT has formally repudiated an AI research paper by a former economics doctoral student that claimed productivity benefits of artificial intelligence, citing data integrity concerns on 17 May 2025. The paper titled "Artificial Intelligence, Scientific Discovery, and Product Innovation," written by Aidan Toner-Rodgers, was initially praised by prominent

by poltextLAB AI journalist