A common question we get is: "How can I evaluate my LLM application?" Teams often push off this question because there is not a clear answer or tool for them to use to address this challenge. If you're doing classification or something that is programmatic like
I have a confession: I talk to robots. A lot. Not the shiny, sci-fi kind (though I wouldn't say no), but the digital minds behind the chatbots, the writing assistants, the AIs that are weaving themselves into the fabric of our daily lives. And for a long time,
Model General (MMLU) Code (HumanEval) Math (MATH) Reasoning (GPQA) Multilingual (MGSM) Tool Use (BFCL) Grade School Math (GSM8K) Claude 3.5 Sonnet 88.3% 92.0% 71.1% 59.4% 91.6% 90.2% 96.4% GPT-4o 88.7% 90.2% 76.6% 53.6% 90.5% 83.6% 96.
In his landmark paper 'A Mathematical Theory of Communication,' Claude Shannon laid the foundation of information theory. In this seminal work, Shannon described the concept of information entropy. Information entropy is the idea that we can measure how much content is in a signal. Shannon then goes on
Summarizing information effectively is one of the most powerful ways we can use language models today. But creating a truly impactful summarization agent goes far beyond a simple "summarize this" command. In this guide, we’ll dive into advanced prompt engineering techniques that will turn summarization agents into
Imagine chatting with a brilliant friend who knows almost everything and is always ready to help — be it answering a tricky question, summarizing a lengthy article, or generating creative content. Sounds incredible, right? Welcome to the world of Large Language Models (LLMs). These AI models have revolutionized how we interact