Shelf Blog: RAG

Acronyms allow us to compact a wealth of information into a few letters. The goal of such a linguistic shortcut is obvious – quicker and more efficient communication, saving time and reducing complexity in both spoken and written language. But it comes at a price – due to their condensed nature...

Effective data management is crucial for the optimal performance of Retrieval-Augmented Generation (RAG) models. Duplicate content can significantly impact the accuracy and efficiency of these systems, leading to errors in response to user queries. Understanding the repercussions of duplicate...

Poor data quality is the largest hurdle for companies who embark on generative AI projects. If your LLMs don’t have access to the right information, they can’t possibly provide good responses to your users and customers. In the previous articles in this series, we spoke about data enrichment,...

While Retrieval-Augmented Generation (RAG) significantly enhances the capabilities of large language models (LLMs) by pulling from vast sources of external data, they are not immune to the pitfalls of inaccurate or outdated information. In fact, according to recent industry analyses, one of the...

Large language models are skilled at generating human-like content, but they’re only as valuable as the data they pull from. If your knowledge source contains duplicate, inaccurate, irrelevant, or biased information, the LLM will never behave optimally. In fact, poor data quality is so inhibiting...

While large language models excel in mimicking human-like content generation, they also pose risks of producing confusing or erroneous responses, often stemming from poor data quality.  Poor data quality is the primary hurdle for companies embarking on generative AI projects, according to...