A look at how to augment LLM results with retrieval augemented generation (RAG) and vector search, directly from BigQuery.

LLM evaluation is the process of assessing the performance and capabilities of LLMs. This helps determine how well the model understands and generates language, ensuring that it meets the specific needs of applications. There are multiple ways to perform LLM evaluation, each with different advantages. In this blog post, we explain the role of LLM evaluation in AI lifecycles and the different types of LLM evaluation methods. In the end, we show a demo of a chatbot that was developed with crowdsourcing.

Despite the transformative potential of AI, many financial planning and analysis (FP&A) teams are hesitating, waiting for this emerging technology to mature before investing. According to a recent Gartner report, a staggering 61% of finance organizations haven’t yet adopted AI. Finance has always been considered risk averse, so it is perhaps unsurprising to see that AI adoption in finance significantly lags other departments.

AI applications that produce human-like text, such as chatbots, virtual assistants, language translation, text generation, and more, are built on top of Large Language Models (LLMs). If you are deploying LLMs in production-grade applications, you might have faced some of the performance challenges with running these models. You might have also considered optimizing your deployment with an LLM inference engine or server.

On April 24, we released Snowflake Arctic with a key goal in mind: to be truly open. In line with that goal, the Snowflake AI Research team is writing a series of cookbooks to describe how to pretrain, fine-tune, evaluate, and serve large-scale mixture-of-experts (MoEs) such as Arctic.

Software developers have plenty to keep them awake at night. Their top concern is no longer how to express the latest algorithm in their favorite language (C, C++, Erlang, Java, etc.). Instead, it’s being replaced by artificial intelligence (AI). Here we take a look at the process for AI writing code and answer the question: Will AI replace programmers? Read along or jump ahead to the section that interests you the most.

The emergence of generative AI (gen AI) heralds a new, groundbreaking era for advertising, media and entertainment. According to a recent Snowflake report, Advertising, Media and Entertainment Data + AI Predictions 2024, gen AI is going to transform the industry — from content creation to customer experience. The companies that will come out ahead during this time are those that most successfully and quickly supercharge their data strategy.