If you are implementing a generative AI solution using Large Language Models (LLMs), you should consider a strategy that uses Retrieval-Augmented Generation (RAG) to build contextually aware prompts for your LLM. An important process that occurs in the preproduction pipeline of a RAG-enabled LLM is the chucking of document text so that only the most relevant sections of a document

In mid-2024, creating an AI demo that impresses and excites can be easy. Take a strong developer, some clever prompt experimentation, and a few API calls to a powerful foundation model and you can often build a bespoke AI bot in an afternoon. Add in a library like langchain or llamaindex to augment your LLM with a bit of custom

Anyone who has worked in a team environment knows that every successful team has one go-to person—that special individual who can help you regardless of the nature of your problem. On a traditional software development team, this individual is an expert programmer and is also an expert in one other technology, which could be a database technology like Snowflake

A chain is as strong as its weakest link - and your AI/ML infrastructure is only as fast as your slowest component. If you train machine learning models with GPUs, then your weak link may be your storage solution. The result is what I call the “Starving GPU Problem.”  The Starving GPU problem occurs when your network or your

Explore the essential role of Data Engineers in unleashing the true power of AI! Data Engineers have a critical foundation in cleaning and structuring raw data for ML success. Learn why their expertise in data infrastructure, feature engineering, and pipeline optimization is indispensable.