Shelf Blog: RAG

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models (LLMs) by pulling from vast sources of external data, they are not immune to the pitfalls of inaccurate or outdated information.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...