adesso Blog
04.12.2024 By Andy Schmidt and Milena Fluck
Prompt Engineering – A Cognitive Approach
Prompts drive thinking, writing, and creativity – in school, at work, or when using AI. The art of formulating clear and effective prompts can be crucial, whether in the classroom or when working with language models. A well-designed prompt guides responses, sets clear expectations, and invites reflection. This blog post shows how ‘prompt engineering’ is a creative, cognitive approach to helping people and machines perform at their best.
Read more28.11.2024 By Nehir Safak-Turhan
The symbiosis of regulation and innovation – regulation as a catalyst for AI in finance?
Regulation is driving AI-based innovations in banking! Even if, at first glance, regulation with its strict rules and requirements is perceived as a brake on innovation, in many cases it sets the rules that ensure legal certainty and orientation. In this blog post, I will take a closer look at the impulses from a regulatory perspective that strengthen this symbiosis.
Read more27.11.2024 By Libero Raspa
Current Areas of Application of AI in the Banking Sector
AI is revolutionising the banking sector and opening up a wide range of opportunities to increase efficiency, better respond to customer needs and meet regulatory requirements. From intelligent chatbots to automated credit processes and real-time fraud detection, AI is changing the way banks operate and interact with their customers. This blog post provides an overview of the current possibilities for using AI in the banking sector in the front and back office, as well as in the regulatory environment.
Read more29.10.2024 By Milena Fluck and Daniel van der Wal
Creativity: Cognition and GenAI - Part 3
In the first two parts of this blog post, we looked at the concept of creativity and the role of humans and their creative potential in the creative process. In this part, we take a broader perspective and look at how some approaches from the field of GenAI work in connection with human cognitive processes.
Read more20.09.2024 By Sascha Windisch and Immo Weber
GraphRAG: Utilising complex data relationships for more efficient LLM queries
Companies and authorities are often faced with the challenge of finding relevant information in huge amounts of data. Although Retrieval Augmented Generation (RAG) is still a relatively new technology for targeted retrieval of local domain knowledge, the technology often fails to aggregate complex distributed information. This is where GraphRAG comes into play. We present it in detail in this blog post.
Read more19.09.2024 By Ellen Tötsch
Down the Rabbit Hole: LLMs and the search for the perfect answer
A lot has happened since the breakthrough for Large Language Models (LLMs) with ChatGPT. What has remained is our desire to supplement these language models with further knowledge. There is no longer a one-size-fits-all solution, but there are numerous possibilities. This blog post provides an overview of the various options for optimising LLMs.
Read more04.07.2024 By Ingo Gregus
AI in marketing in the insurance industry: not getting involved excluded (Part 1)
This is the first part of my three-part blog post on the role of artificial intelligence (AI) in the insurance industry. In this first part, I focus on the general opportunities and challenges of AI as well as specific applications of generative AI. The focus is on marketing and sales.
Read more25.06.2024 By Maria Selimbegovic
Becoming a Machine Learning Expert with adesso’s training offer
Everyone makes GenAI and that's right! But despite the hype surrounding GenAI, the fundamental field of machine learning (ML) must not be forgotten. Because there is still a lot of untapped potential for companies here. Few people understand what artificial intelligence (AI) actually includes and how to use it optimally for their own business. We have gained a lot of experience as trainers and based on this we have created suitable training offers.
Read more04.06.2024 By Rafael Dubach
Revolutionizing AI with adesso's RAG model
Discover adesso's Retrieval-Augmented Generation (RAG) Model, a breakthrough in AI that enhances large language models (LLMs) with up-to-date, external knowledge. This model promises smarter, cost-effective AI solutions by dynamically adapting to new information.
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