Blog posts

Navigate open enrollment with AI21’s JambaCare. Compare health insurance plans side-by-side, leveraging Jamba's 256k context window for personalized insights.

Explore the top six challenges enterprises face in AI integration, including knowledge gaps, data privacy, and unrealistic expectations.

Get started with AI21's Conversational RAG endpoint with this tutorial on how to build a financial analyst assistant for in-depth research on your collection of documents.

Snowflake customers can now build with Jamba-Instruct directly in their secured and governed Snowflake AI Data Cloud using just a simple SQL statement, giving them access to the longest context window available in Snowflake Cortex AI.

AI21 Labs Global Head of Sales, Ofir Zan, breaks down some of the most common uses for long context models he sees across financial services, healthcare, customer support, and retail.

Our debut Jamba hackathon, in partnership with AGI House, brought together talented developers to explore use cases using Jamba’s 256K context window. Here are some highlights.

AI21 Labs Co-CEO Yoav Shoham on how we built Jamba-Instruct to close the gap between claimed and effective context window length and to efficiently serve long context workflows.

For the past six years, Transformers have dominated the AI world, achieving remarkable success in tasks from translation to text generation. But their time may soon be up – is attention really all we need?

As a Task-Specific Model, Contextual Answers is optimized to excel in question answering, outperforming leading Foundation Models across three metrics.

AI21 outperforms major LLM developers on transparency, making it the vendor of choice for enterprises looking to build reliable AI systems.

Automate and streamline the term sheet creation process with AI21’s Jamba-Instruct and Contextual Answers models.

Learn about some of the key challenges facing organizations when deploying enterprise LLM, and how mature LLM implementations address these challenges.

Learn about the concerns that organizations are facing today as AI adoption increases, and how enterprise AI is evolving to meet them.

How do companies use Generative AI as a strategic tool for tackling real business challenges? Start with the problems today’s GenAI applications can solve. Scoping high-impact use cases helps overcome common roadblocks facing enterprise teams. It’s also a practical start for a smooth GenAI integration.

The AI21 Hackathon with AWS highlighted GenAI’s potential to revolutionize various industries, especially when taking advantage of AI21 and AWS’ joint capabilities.

AI21’s Asaff Zamir talks with Guy Ben Porat, VP of Discovery & Delivery Solutions at Clarivate, about how the collaborative partnership with AI21 led to a successful implementation process.

By grounding AI in an organization's unique expertise, Retrieval-Augmented Generation (RAG) helps enterprises overcome hurdles in deploying large language models. AI21's RAG Engine provides advanced retrieval capabilities without enterprises having to invest heavily in development and maintenance.

Forward-thinking retailers are racing to deploy LLM chatbots to hyper-personalize the buying journey and cement customer loyalty.

2024 will be a pivotal year as enterprises shift their focus from generative AI experimentation to practical implementation. There will be growing pains, but those who navigate them successfully will gain a real competitive advantage. 

We've identified gaps where Generative AI can create fast, tangible impact for businesses, starting with their customer support teams. Learn how our Contextual Answers system reduces the need for large support teams, while improving customer satisfaction.

In this guide, we breakdown the Large Language Model (LLM) product development process. You'll learn all the necessary stages of developing an LLM-based product, including planning, execution, and monitoring. 

When it comes to optimizing websites for SEO, ecommerce businesses face even greater challenges than regular companies. This guide offers a deep dive into common ecommerce SEO challenges and how Generative AI can help overcome them.

Artificial intelligence is already deeply embedded in the finance industry, however when it comes to Generative AI, companies are just beginning to scratch the surface. In this article, we will delve into use cases in which Generative AI has the highest potential to revolutionize the finance industry.

The adoption of generative AI in the retail industry will empower retailers to enhance user experiences, streamline operations and ultimately drive business growth. As generative AI continues to evolve, retailers must embrace the latest AI technology in order to stay competitive in the rapidly changing market landscape.

In this post, we will discuss the major opportunities for the retail and ecommerce industries in 2023, and how companies in these sectors can apply generative AI to scale operations, drive more sales, automate workflows, boost productivity and gain an advantage in an increasingly competitive landscape.