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

Django is one of the leading Python frameworks used to create full-stack web applications. In this comprehensive guide, you will explore the intricacies of testing and debugging within the Django framework, focusing on advanced methodologies and essential tools. Beginning with the fundamentals of Django unit and integration testing, you will delve into advanced techniques such as mocking, testing middleware, and profiling for optimal performance.

For flawless digital experiences, quality engineering (QE) is more than just a technical requirement—it’s the driving force behind innovation and success. Steve Semelsberger, Testlio’s CEO, will share his valuable insights in an engaging LinkedIn series designed to help fellow CEOs unlock the strategic benefits of QE. In this series, Steve will address your toughest questions and demystify the complexities of building a robust QE framework.

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.

A database schema, or DB schema, is an abstract design representing how your data is stored in a database. Database schemas can be visually represented using schema diagrams, such as the one below: A database schema diagram visually describes the following: Database schemas are at the heart of every scalable, high-performance database. They’re the blueprint that defines how a database stores and organizes data, its components’ relationships, and its response to queries.

Imagine transforming raw business data into actionable insights with minimal effort. This is the value proposition of automated analytics, a form of data analytics fast becoming more accessible among modern business intelligence (BI) and analytics software solution vendors. Independent software vendors (ISVs) and enterprise organizations at the cusp of investing in analytics struggle with manual data processes that are time-consuming and prone to errors.