Generative AI and more...

Correre troppo avanti a volte non conviene... (TLDR). Questa volta voglio raccontarvi una storia un pizzico diversa (ed infatti lo faccio nella mia bella lingua nativa). Ma comunque una storia che ha a che fare con l'innovazione e l'AI. Parto da lontano: quando sono venuto a Roma per la prima volta, dopo la laurea, dividevo la camera di albergo con un carissimo Ingegnere di Bologna: Leo (eh, avessi seguito i suoi consigli, sopratutto in un certo campo... ). Era un genio dell'elettronica digitale! Dopo un po',  andò a lavorare in una multinazionale dell'elettronica, un gigante dell'epoca. Ma in un posto sperduto, caro a I. Silone. Quando ci incontravamo mi regalava le calcolatrici che usavano per i test di "resistenza", le lanciavano da X metri di altezza e dovevano sopravvivere (non i lanciatori, le calcolatrici). Un giorno mi racconto', un po' depresso, che il suo responsabile gli aveva detto di andare più piano. Di non essere sempre cosi avan...

Improving the quality of your Generative AI Solution using RAG and Oracle OCI Data Science. Since the launch of ChatGPT , in November 2022, we have seen widespread interest in its innovative technology, particularly in its GPT-4 iteration. Since that moment, we have come to understand the capabilities of Large Language Models (LLMs) and we have entered a new era, where the Generative AI potential of these models can significantly improve both productivity and quality, across numerous professions We have learned that: LLM can answer our questions LLM can write emails for our customers LLM can perform copy-editing of our papers (like this one) LLM can quickly generate code snippets for prototype .... But, although LLMs have shown great promises, they also have limitations, especially in the Enterprise sector. At the beginning of this year, a really  good review paper was published, as usual, on Arxiv. In the paper they highlight, among others, these limitations: LLMs have limited k...

 Well, in a RAG chain we have always several important pieces: the Embeddings Model , which translates texts in dense vectors, enabling Semantic Search the Vector Store , where you safely save your texts and vectors, giving you a fast way to find all relevant docs A Reranker , which helps to refine your search The Large Language Model (LLM) In the beginning, you could think that the LLM is only useful at the end of the chain , when all the docs retrieved are put inside the context of the prompt, together with your request. There, it synthetizes the answer. But all the evolution we see today is often based on ideas on how to use more and more of the incredible power we have in current LLMs. Let us consider one important feature we want to have in a Knowledge Assistant: we want the assistant to keep the memory of all the previous questions and answers (message history) and use it to enable a more natural kind of conversation. For example, imagine that one of your questions is: "...

Happy New Year: it is strange to think that the New Year should be always better than the previous one. But at the beginning, we should think so. Year 2023 for some reason has been a good year, for me and other people. For other reasons, it has been a difficult year, for me and other people. But, I always tend to think that, if used correctly, new technologies can always, in some way or other, improve our work and maybe our lives. This end of the year has been a wonderful period for AI and especially Generative AI , with great news coming out almost every day. In this blog, I want to share with you all my excitement and all the interesting things that I'll discover and find on my way to this fascinating world.  I live in Rome, Italy.  Most of the new things in this field are built, trained, and tested on English documents. They tend to work better in English, a little less in Italian (and French, Dutch, ...).  Therefore, from time to time, I'll enter into the details reg...