OpenAI function calling provides developers with a new level of precision and control when integrating language models into real-world applications. OpenAI function calling allows AI systems to interact with APIs in a structured, reliable, and manageable way. Developers can orchestrate interactions through external functions for better results that are easier
"Prompt orchestration," refers to the structured coordination of LLM prompts to guide AI models through complex tasks with accuracy and consistency. While AI models have unlocked new levels of automation and insight, their true value depends on how effectively humans can direct them—especially when tasks require more
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
When you're having trouble getting one prompt to work, try splitting it up into 2, 3, or 10 different prompt workflows. When prompts work together to solve a complex problem, that's an AI agent. What Are AI Agents and What Are They Used For AI agents
Setting up a mistral system prompt is essential for anyone looking to get the most accurate and relevant results from Mistral AI’s language models. Creating effective prompts requires careful attention to detail—each instruction you provide directly shapes the model’s response. Whether your goal is to generate concise
Artificial intelligence is actively shaping how we work, learn, and create every day. If you’re searching for information about Gemini 2.5, Google’s latest AI model, this guide will show you how to unlock its full potential in both the Gemini Web App and Google AI Studio. Whether
When working with AI language models, the quality of your prompts directly shapes the results you get. If you’re searching for the best prompt manager to organize, optimize, and collaborate on AI prompts, this guide will help you navigate the top solutions available. As projects expand, managing dozens of
Claude MCP (Model Context Protocol) is transforming how AI models connect with external tools and data sources. Developed by Anthropic, this open standard introduces a universal interface that enables AI systems to access real-time data, perform tasks, and interact with various APIs seamlessly. Claude MCP is a groundbreaking advancement in
Prompt chaining is a method that significantly enhances the performance of large language models (LLMs) and prompt chainers are tools that make chaining easier for you. By breaking down complex tasks into manageable prompts, this process guides models to ensure precision and reduce errors in tasks like document summarization and
The question "Is prompt engineering dead?" is on the minds of many as artificial intelligence continues its rapid advancement. Prompt engineering, the art and science of crafting effective inputs to guide AI models towards producing desired outputs, is at a crucial turning point. As AI evolves at an
Two AI tools have captured the attention of both developers and general users: Grok, created by Elon Musk’s xAI, and ChatGPT, developed by OpenAI. While both of these tools are designed to enhance human productivity through AI, they each approach this goal with distinct methodologies, capabilities, and features. In
Ollama and Hugging Face are two leading platforms in artificial intelligence (AI) and machine learning (ML), each offering distinct features tailored to various needs. Ollama is a great option for privacy and low-latency offline applications, while Hugging Face excels in scalability and access to a wide range of cloud-based models.
Introduction To harness the potential of large language models (LLMS), organizations must effectively monitor and manage their usage statistics. This involves tracking performance metrics, user engagement, and model efficiency to ensure these models deliver value effectively. This article explores top tools and best practices for maximizing LLM usage statistics, offering
Is PromptLayer a LangSmith Alternative? Yes, PromptLayer serves as a great alternative to LangSmith for teams building applications with Large Language Models (LLMs). How do PromptLayer and LangSmith compare? Both PromptLayer and LangSmith offer platforms designed to help developers manage the lifecycle of LLM applications. They provide capabilities for prompt
The surge in AI adoption brings a new layer of complexity in application development, extending beyond traditional software code to include prompts, models, parameters, and configurations. Just as version control has become an indispensable practice in software development for managing code changes, a similar approach is crucial for navigating the
From powering sophisticated chatbots and virtual assistants to enabling advanced content generation and complex data analysis, LLMs are becoming integral to modern business operations and technological innovation. This widespread adoption highlights a critical need: effective, standardized ways to evaluate these powerful models. Robust evaluation is essential. It helps us understand
The shift toward remote and hybrid work has sparked a surge in digital collaboration tools, with artificial intelligence playing a central role. AI has moved from a futuristic concept to an everyday asset, reshaping how teams work and communicate. Both established names and newer entrants are competing to roll out
Within the broad field of AI, two terms frequently come up: Large Language Models (LLMs) and Generative AI. While they're related, they aren't interchangeable. Large Language Models (LLMs) are a specialized subset of Generative AI focused on text-based tasks, while Generative AI encompasses a wider range
While cloud-based LLM services are popular, running LLMs locally offers benefits like enhanced privacy, reduced latency, and greater control over data. Two prominent platforms that enable local LLM deployment are LM Studio and Ollama. LM Studio prioritizes ease of use with a polished GUI ideal for beginners, while Ollama offers
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