Large Language Models (LLMs) are not effective for predictions; combining them with machine learning models enhances enterprise accuracy.

We need to adjust our infrastructure to effectively work with LLMs, as they are fallible systems that require careful integration.

Combining deterministic machine learning models with probabilistic large language models (LLMs) helps us reach the accuracy levels we target.

Performance issues in graph-based systems often arise when scaling to enterprise levels, affecting both accuracy and system reliability.

Evaluating the performance of applications built with large language models is challenging because the use cases are often subjective, making traditional accuracy metrics insufficient.

Using LLMs as judges for evaluating outputs is promising, but it requires careful prompt engineering to ensure accurate grading of responses.

Successful AI implementations in enterprises involve collaboration between business users and IT, ensuring that both the technical and operational aspects are addressed effectively.

Achieving 100% accuracy in AI applications is crucial, especially in legal contexts where providing incorrect information can have serious consequences for lawyers handling important cases.

As we scale large language models (LLMs), we will eventually encounter a data wall, which limits the effectiveness of current scaling techniques.