Benchmarking Domain Intelligence
Large language models are improving rapidly; to date, this improvement has largely been measured via academic benchmarks. These benchmarks, such as MMLU and...
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AI Agent Systems: Modular Engineering for Reliable Enterprise AI Applications
Monolithic to Modular The proof of concept (POC) of any new technology often starts with large, monolithic units that are difficult to characterize...
The Long Context RAG Capabilities of OpenAI o1 and Google Gemini
Retrieval Augmented Generation (RAG) is the top use case for Databricks customers who want to customize AI workflows on their own data. The...
Generating Coding Tests for LLMs: A Focus on Spark SQL
Introduction Applying Large Language Models (LLMs) for code generation is becoming increasingly prevalent, as it helps you code faster and smarter. A primary...
Long Context RAG Performance of LLMs
Retrieval Augmented Generation (RAG) is the most widely adopted generative AI use case among our customers. RAG enhances the accuracy of LLMs by...
Enhancing LLM-as-a-Judge with Grading Notes
Evaluating long-form LLM outputs quickly and accurately is critical for rapid AI development. As a result, many developers wish to deploy LLM-as-judge methods...
Open Sourcing Unity Catalog
We are excited to announce that we are open sourcing Unity Catalog, the industry’s first open source catalog for data and AI governance...
Introducing AI/BI: Intelligent Analytics for Real-World Data
Today, we are excited to announce Databricks AI/BI , a new type of business intelligence product built from the ground up to deeply...
Unity Catalog Lakeguard: Industry-first and only data governance for multi-user Apache Spark™ clusters
We are thrilled to announce Unity Catalog Lakeguard , which allows you to run Apache Spark™ workloads in SQL, Python, and Scala with...
DSPy on Databricks
Large language models (LLMs) have generated interest in effective human-AI interaction through optimizing prompting techniques. “Prompt engineering” is a growing methodology for tailoring...