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AI

Streamline Your AI Integration: A Deep Dive into Kong AI Gateway

Join us to learn about the AI Gateway concept and explore the rapidly evolving landscape of large language models (LLMs) in modern applications. With the surge of AI providers and the lack of standardization, organizations face significant challenges in adopting and managing AI services effectively. Kong's AI Gateway, built on the proven Kong Gateway platform, addresses these challenges head-on, empowering developers and organizations to harness the power of AI quickly and securely.

Snowflake Launches the World's Best Practical Text-Embedding Model for Retrieval Use Cases

Today Snowflake is launching and open-sourcing with an Apache 2.0 license the Snowflake Arctic embed family of models. Based on the Massive Text Embedding Benchmark (MTEB) Retrieval Leaderboard, the largest Arctic embed model with only 334 million parameters is the only one to surpass average retrieval performance of 55.9, a feat only less practical to deploy models with over 1 billion parameters are able to achieve.

LLM Metrics: Key Metrics Explained

Organizations that monitor their LLMs will benefit from higher performing models at higher efficiency, while meeting ethical considerations like ensuring privacy and eliminating bias and toxicity. In this blog post, we bring the top LLM metrics we recommend measuring and when to use each one. In the end, we explain how to implement these metrics in your ML and gen AI pipelines.

Unleashing the Power of Digital Assurance in the Age of AI and Gen AI: Charting the Way Forward

In the pursuit of excellence, Quality Assurance (QA) has embarked on a profound journey of automation. Beginning with manual testing as its foundation, QA has progressed steadily through functional automation and smart automation, culminating in its embrace of Intelligent automation and Codeless automation. This evolution mirrors our transition from traditional waterfall models to agile methodologies.

Why RAG Has a Place in Your LLMOps

With the explosion of generative AI tools available for providing information, making recommendations, or creating images, LLMs have captured the public imagination. Although we cannot expect an LLM to have all the information we want, or sometimes even include inaccurate information, consumer enthusiasm for using generative AI tools continues to build.

Have You Heard of Devin the AI Software Engineer??

Imagine a world where every piece of digital content can be verified and traced back to its source. Lindsay Walker, Product Lead at Starling Lab for Data Integrity, walks us through the emerging tools that could make this possible. While AI tools hold incredible potential for good, Lindsay also warns against threats and countermeasures needed to keep our virtual representations safe. She emphasizes the need to build provenance into tools, discusses blockchain use cases, and shares systems that implement hashes for security.

The Sliding Doors for Responsible AI

In this blog series, I have been exploring the “sliding doors” – or divergent paths - organizations can take with data and analytics. Sometimes, grabbing the wrong door means missing out on creating the most value with your data. But in some instances, it can also lead you on a more serious path of breach of compliance with regulations.

A Breakthrough AI-Powered SQL Assistant

Data is the lifeblood of modern businesses, but unlocking its true insights often requires complex SQL queries. These queries can be time-consuming to write and challenging to maintain. At Snowflake, we believe in making the power of data accessible to all. That’s why we prioritize simplicity, governance and quality in everything we build – including our AI-powered tools.