Two prominent platforms that allow teams to manage, evaluate, and iterate on prompts effectively in the AI space are Braintrust and PromptLayer. While both aim to improve the LLM development workflow, they cater to different team structures and skill sets. This article provides a comparative analysis of Braintrust and PromptLayer,
One persistent challenge in AI has been connecting powerful LLMs to the vast array of external data sources and tools necessary for real-world applications. Anthropic's Model Context Protocol (MCP), introduced in late November 2024, offers a promising solution. In short, MCP is an open, standardized client-server architecture designed
As LLMs find applications in different sectors, it's become crucial to enable them to interact with the real world and perform tasks beyond simple text generation. Two primary methods have emerged to address this need: LLM Agents and Function Calling. In short, LLM Agents are modular AI systems
The OpenAI API Playground is a fantastic tool for initial experimentation with OpenAI's models. It allows developers to quickly test prompts, adjust parameters, and see immediate results without writing code. However, for serious application development, the Playground has limitations in prompt management, experiment tracking, and team collaboration. This
AI agents are programs that can perform tasks, make decisions, and even learn, all with a degree of autonomy. This guide provides a beginner-friendly introduction to the world of AI agents, explaining core concepts and walking you through building your first agent using Python. Table of Contents * Introduction * Basic AI
Developing applications powered by LLMs presents unique challenges: managing context, integrating external data, orchestrating multi-step reasoning, and ensuring scalability. This is where frameworks like AutoGen and LangChain come in. AutoGen (developed by Microsoft) and LangChain are two popular open-source frameworks designed to simplify the development of LLM-powered applications. While both
Anthropic’s Claude 3.7 Sonnet and OpenAI’s O1 represent the latest advancements in AI reasoning, pushing the boundaries of mathematical logic, coding proficiency, and scientific analysis. While Claude 3.7 Sonnet boasts an adaptive reasoning approach optimized for real-world applications, OpenAI O1 is engineered for rigorous multi-step problem-solving
New LLMs emerge frequently, each boasting improvements in performance, cost-effectiveness, or specialized capabilities. As a prompt engineer, how can you harness the power of these advancements without being locked into a single model or constantly rewriting your prompts? The answer lies in model-agnostic prompts. Model-agnostic prompts are versatile instructions designed
Artificial intelligence has rapidly evolved, with large language models (LLMs) and other generative AI tools becoming increasingly accessible. But to truly harness the power of these models, from ChatGPT to image generators, you need to master the art of the prompt. This guide provides a comprehensive overview of prompt engineering,
On February 17th, xAI’s Grok 3 made its debut into the world, to much fanfare. In a livestream demo, it was revealed that Grok 3 is being deployed with a reasoning model. So, it’s natural to want to compare it with other, similar reasoning models such as OpenAI’
Prompt chaining is an AI technique that enhances the capabilities of large language models (LLMs). It involves breaking down a complex task into a series of interconnected prompts, where the output of one prompt becomes the input for the next. This structured approach guides the LLM through a more nuanced
Have you ever wondered how Large Language Models (LLMs) fine-tune their responses to be more creative or focused? The answer lies in understanding the "temperature" setting. This comprehensive guide will look into the intricacies of temperature in LLMs, exploring its impact, applications, and best practices. What is Temperature