Jonathan Pedoeem

268 posts

LLM Usage Statistics: Top Tools and Best Practices

Introduction to {{item.name}} 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

How to Evaluate LLMs: Methods, Metrics & Tools for Assessment

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

Best AI Collaboration Tools

The shift toward remote and hybrid work has accelerated demand for reliable digital collaboration tools, and AI now plays a practical role in everyday engineering workflows. For teams building and shipping LLM-powered applications, prompts, agents, and AI workflows, AI has moved from a futuristic concept to a daily asset

LM Studio vs. Ollama: Choosing the Right Local LLM Platform

While cloud-based LLM services remain popular, running LLMs locally can offer practical benefits for AI teams, including stronger data privacy, lower latency, clearer data residency, and more direct control over models, prompts, and infrastructure. Two prominent platforms that enable local LLM deployment are LM Studio and Ollama. LM Studio

What is Anthropic's Model Context Protocol (MCP)?

One persistent challenge in AI development has been reliably connecting powerful LLMs to the external data sources and tools required for real-world applications. Anthropic's Model Context Protocol (MCP), introduced in late November 2024, offers a promising approach. At a high level, MCP is an open, standardized client-

LLM Agents vs. Function Calling: An Analysis of Techniques

As LLMs are adopted across industries, it has become crucial to let them interact with real-world systems and perform tasks beyond simple text generation. Two primary methods address this need: LLM Agents and Function Calling. In short, LLM Agents are modular AI systems designed for complex, multi-step reasoning

OpenAI API Playground: Explore & Test AI Models

The OpenAI API Playground is a useful tool for early experimentation with OpenAI models. It helps developers and AI teams prototype prompts, adjust model settings, compare outputs, and inspect request and response payloads before moving prompts into LLM-powered application code, agents, or production workflows. However, for serious application development,

Building Your First AI Agent: A Beginner's Guide

AI agents are programs that can use models, tools, and context to perform tasks, make decisions, and adapt their behavior with a degree of autonomy. This guide provides a beginner-friendly introduction to AI agents, explains the core concepts behind LLM-powered workflows, and walks you through building your first

AutoGen vs. LangChain: Choose the Right Framework

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

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