Generative AI
Continuously Monitor Your RAG System to Neutralize Data Decay
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,...
5-Point RAG Strategy Guide to Prevent Hallucinations & Bad Answers This guide designed to help teams working on GenAI Initiatives gives you five actionable strategies for RAG pipelines that will improve answer quality and prevent hallucinations.
Fix RAG Content at the Source to Avoid Compromised AI Results
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models (LLMs) by pulling from vast sources of external data, they are not immune to the pitfalls of inaccurate or outdated information.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...
Generative AI in Healthcare: A Balance between Benefits and Ethics
It’s estimated that $1 trillion in healthcare spending is wasted each year in the U.S. By automating routine tasks and making more use of clinical data, GenAI is a new opportunity to optimize healthcare expenditures and unlock part of the money lost to inefficiencies. It could organize...
Strategic Data Filtering for Enhanced RAG System Accuracy and Compliance
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...
Shield Your RAG System from these 4 Unstructured Data Risks
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...
These Data Enrichment Strategies Will Optimize Your RAG Performance
Large language models have an impressive ability to generate human-like content, but they also run the risk of generating confusing or inaccurate responses. In some cases, LLM responses can be harmful, biased, or even nonsensical. The cause? Poor data quality. According to a poll of IT leaders by...
Generative AI and Data Preparation in an Integrated AI Strategy
Identifying how Generative AI and data preparation fits into your business case is a complex endeavor. If you are feeling overwhelmed trying to keep up with emerging AI technologies and applications — and it’s almost 100% likely that you are—you are not alone. Because “almost 100%” by definition...
What are Neural Networks and How Do They Work With Generative AI
Neural networks involve a series of algorithms designed to recognize patterns, interpret data, and make decisions or predictions. They are modeled loosely after the human brain’s architecture. Neural networks have become a cornerstone of AI technologies alongside others, such as rule-based...
Human in the Loop & Generative AI: Balancing AI Automation with Human Insight
Generative artificial intelligence (GenAI) has emerged as a powerful tool for content creation, but often requires a human in the loop to ensure the outputs are valuable. The value of GenAI includes productivity gains, opportunity for revenue growth, and better accuracy for your organization’s...
Top 6 Takeaways from the OpenAI Conference
OpenAI introduced artificial intelligence (AI) to the general public and their latest keynote emphasized their influence on the development of this new technology. Since Chat-GPT was unveiled in November 2022, every major tech company has announced their own AI initiative, but OpenAI remains the...
Future-Proofing Business for AI and Automation: Risk, ROI, and the Strategy of Disruption
In our first entry in our series of future-proofing for artificial intelligence (AI), we discussed the inevitability of AI disruption and how your organization should prepare to manage change. In this second entry in the series, we focus on what your AI strategy needs to manage risk, evaluate ROI,...