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Prompt engineering has become foundational in the development of advanced applications powered by Large Language Models (LLMs). As prompts have grown in complexity—incorporating dynamic components, multiple roles, structured data, and varied output formats—the limitations of unstructured text approaches have become evident. Microsoft released Prompt Orchestration Markup Language (POML) , a novel open-source framework designed to bring order, modularity, and extensibility to prompt engineering for LLMs. Read more:  https://www.marktechpost.com/2025/08/13/microsoft-releases-poml-prompt-orchestration-markup-language/ Potrebbe anche servirti "POML su Github":  https://github.com/microsoft/poml?tab=readme-ov-file

When we interact with LLMs, we typically use natural language, typing a paragraph and hoping the model understands our intent. This approach works until it fails, due to unclear instructions, missing context, or formatting issues that confuse even capable systems. JSON prompting is an emerging technique that uses structured data instead of free-form text. By organizing instructions, examples, and constraints into a JSON object, we sacrifice some conversational warmth for greater precision. The result is a prompt that is both human-readable and easily parsed by code. This article explains why this matters, how JSON prompts differ from standard prompts, and provides a step-by-step guide for crafting them effectively. Read more: https://www.analyticsvidhya.com/blog/2025/08/json-prompting/

Le previsioni sull’agentic AI, quella che dovrebbe svolgere in autonomia alcune attività al posto nostro, sono molto meno positive di quanto speravano i suoi produttori La notizia è di quelle che fa subito rumore. La società di consulenza strategica Gartner ha rilasciato il suo ultimo report e le sue previsioni sul futuro dell’ intelligenza artificiale agentica sono state piuttosto negative. Secondo un’analisi del mercato attuale, il 40% dei progetti nel campo verrà abbandonato per entro la fine del 2027 . I motivi sono diversi, ma in cima alla lista troviamo tre ragioni principali: la lievitazione costi, un valore commerciale poco chiaro e un controllo dei rischi inadeguato. In una parola, in molti casi si tratta di hype , clamore. Leggi tutto: https://www.wired.it/article/intelligenza-artificiale-agentica-tendenze-mercato/