Insights, tips, and product updates

Let's deep dive into Llama 3. This article will provide a breakdown of what Meta has released about its training data so we can better understand how it was trained and what processes were used to build the high-performing LLM.

While standard LLMs excel in generalized tasks, real-world applications demand robust and domain-specific models. Thus, practitioners must fine-tune them on domain-specific data to suit downstream real-world tasks. This post explores fine-tuning LLMs, its benefits, challenges, approaches, and the steps involved.

If you're wondering whether your company should invest in a data labeling tool, keep reading. We've gathered the most common reasons why companies hesitate or decide to make the switch.

In our latest webinar recap: learn how insurance companies overcome training data challenges to ship their models faster.

Labeling videos is no easy task. To make it as efficient as possible for all our users, we’re happy to introduce an entirely new version of our video labeling tool! Read on to discover our new features.

A complete tutorial on video annotation using machine learning. Find how label video clips and get the best performance from your video annotation projects.

Today, we are pleased to unveil an advanced feature in our video annotation tool that supports long videos with thousands of objects in each frame.

AI document analysis helps businesses to process more documents at greater speeds, uncover patterns within data, combat fraud, and reduce errors.

Even with the advancements in AI, information extraction remains fraught with challenges. In this article, we will tackle the challenges of information extraction and the possible solutions to overcome them.

The use of artificial intelligence (AI) has become widespread. In today's world, artificial intelligence can be found in just about every aspect of technology, including vehicle insurance.

Master the best practices for data labeling, understand the impact of high quality data, and elevate your AI applications in the insurance industry.

Banks are amongst the most regulated establishments in the world. Discover how LCL built a powerful ID information extraction algorithm using Kili Technology to classify the IDs of their customers.

Named entity recognition & text classification are used to help companies understand and process natural language automatically. Read on to learn how.

Large Language Models for named entity recognition are a powerful tool that can save time and resources. Learn how to leverage the power of pre-trained language models with appropriate prompt design to perform NER on any named entity category without requiring task-specific training data.

Large Language Models for named entity recognition are a powerful tool that can save time and resources. Learn how to leverage the power of pre-trained language models with appropriate prompt design to perform NER on any named entity category without requiring task-specific training data.

What's the best data labeling services price that machine learning teams could ask for? We compare 8 top labeling providers for answers.

With the constant rise and use of technology, Artificial Intelligence (AI) has become a great companion to compliance. Compliance is one of the biggest playing fields and plays a pivotal role in banking institutions. How can you as a business maximize its value?

Unlock the true potential of Chat GPT! Explore its inner workings and abilities, discover pro-tips for effective prompting, and learn how to leverage LLMs for optimal labeling. Uncover its potential as a labeler, QA programmer, and asset creator while understanding its limitations.

This blog post delves into the essence of fine-tuning LLMs, drawing insights from our recent webinar and presentation on the subject.

Discover the power of Large Language Models (LLMs) in practical applications and learn how to evaluate them using Kili Technology. Delve into our webinar recap on building conversational engines with LLMs.