adesso Blog

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.

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.

In the world of data processing, there are various approaches to improving efficiency and accuracy. One particularly promising approach is the use of Large Language Models (LLMs) to improve the linking of entities through entity linking. In this blog post, I will highlight the new possibilities and advantages of this technology.

Artificial intelligence is developing rapidly and Retrieval Augmented Generation (RAG) in particular has attracted a lot of attention recently. Large language models such as ChatGPT show their full potential when they are enriched with domain-specific knowledge through RAG. Despite this potential, users often face challenges. In this blog post, we look at the transition from basic to advanced RAG approaches and show how typical problems can be overcome.