So you need a team to build an LLM multi-agent system... how do you interview candidates? I'll try to provide some ideas and strategies in this article. Firstly... What is an AI Engineer? AI engineers build the future. They create scalable AI systems and agents. They test, evaluate,
The term "prompt engineering" really exploded when ChatGPT launched in late 2022. It started as simple tricks to get better responses from AI. Add "please" and "thank you." Create elaborate role-playing scenarios. The typical optimization patterns we all tried. As I've written
Building conversational AI agents is hard. Ensuring they perform reliably across diverse scenarios is even harder. When your agent needs to handle multi-turn conversations, maintain context, and achieve specific goals, traditional single-prompt evaluation methods fall short. In this guide, I'll walk you through best practices for evaluating conversational
Most companies are drowning in data but sending generic outreach that makes you want to hit unsubscribe. This SaaS unicorn with a 100+ person sales team figured out how to use PromptLayer to auto-craft millions of fully-personalized outbound emails at roughly $0.002 each, while keeping both engineers and marketers
A growth marketing startup specializing in e-commerce faced a significant challenge: personalizing cold outreach at massive scale—covering over 30,000 domains and 90,000 contacts—without excessive copywriting costs. The challenge was compounded by fragmented data sources—including website scraping data, SMS messaging frequency, tech stack details, and funding
The real innovation that separates post-ChatGPT UX from pre-ChatGPT UX isn't about chatbots. It's not about intelligence or even about AI thinking through and reasoning. It's about assumptions. In traditional software, users must explicitly provide every piece of information the system needs, but AI-powered
For two years, Midpage has used PromptLayer to transform how they build legal AI, putting lawyers next to engineers to own prompt quality. Their approach has scaled from manual tracking in Notion to automated evaluation pipelines that catch regressions before they reach users. * 80 production prompts across 10 AI features
NoRedInk has been on a mission to unlock every writer's potential since 2012. Today, their adaptive writing platform serves 60% of U.S. school districts and millions of students worldwide. But when they decided to build an AI grading assistant, they faced a challenge that many EdTech companies
Artificial intelligence is rapidly transforming software development, influencing how code is written, tested, and deployed. Developers searching for the top AI dev tools in 2025 will find a diverse set of solutions designed to simplify workflows, boost creativity, and solve complex problems. This article explores the leading options, comparing their
Teams across industries—from marketing to finance—seek new ways to leverage AI, and no code LLM AI platforms eliminate technical roadblocks. These no code solutions empower teams to create LLM-driven applications in minutes, no developer required. They let non-technical users design, test, and launch powerful language-model apps with visual
Developers today have access to powerful frameworks that make building, managing, and connecting AI agents more accessible than ever. This article offers a comprehensive look at the differences between OpenAI Agents SDK and Anthropic’s Model Context Protocol (MCP), helping you decide which toolkit best fits your needs. OpenAI Agents
Setting up a Claude MCP (Model Context Protocol) server is a way to build secure, scalable AI integrations. This guide takes you from initial planning to live troubleshooting, providing actionable best practices and clear definitions along the way. You’ll find every major step explained simply. Let’s get started.
Selecting tools for AI application development is a consequential decision. The infrastructure you choose directly influences product quality, iteration speed, and operational reliability. Braintrust and LangSmith address similar needs—yet their approaches, capabilities, and intended audiences diverge in meaningful ways. Below, we examine their differences with clarity and precision. Curious
Building reliable AI agents is difficult because minor errors multiply quickly when prompts are connected. An AI agent is a software system that autonomously performs tasks on behalf of a user or another system, often using reasoning, planning, memory, and available tools to achieve goals with minimal human intervention. The
It is a complex task to build, refine, and manage complex AI systems at scale. As LLM applications mature from early experiments to business-critical infrastructure, the selection of engineering platforms becomes pivotal—defining workflows, influencing innovation, and shaping cost and reliability. This is a direct, clear comparison of three top
Langtrace and Langfuse are leading open-source observability platforms for large language model (LLM) applications, each with distinct strengths and design philosophies. Langtrace emphasizes standards-based tracing via OpenTelemetry, granular metrics, and enterprise-grade security compliance, making it well-suited for regulated industries and transparent monitoring. Langfuse focuses on collaborative prompt management, rich analytics,
The 2025 State of AI Engineering Survey by Barr Yaron from Amplify Partners offers a comprehensive snapshot of how engineering teams are building, managing, and scaling LLM-powered applications in production. With responses from 500 practitioners, the survey reveals critical insights about the rapid pace of model and prompt iteration, the
Machine learning evolves rapidly, especially as Large Language Models (LLMs) become more advanced. For ML engineers, these developments create significant opportunities and introduce a distinct set of technical challenges. Building, deploying, and maintaining LLM applications requires specialized tools—from prompt management and evaluation to monitoring and experiment tracking. This guide
Let's be honest—you can tinker with prompts in a sandbox all day, but prompt quality plateaus quickly when you're working in isolation. The uncomfortable truth is that only real users surface the edge cases that actually matter. And here's the kicker: the LLM
Automation platforms have evolved from simple “if-this-then-that” scripts to sophisticated ecosystems that connect hundreds of applications, handle complex logic, and integrate AI capabilities. As organizations look to streamline processes, reduce manual work, and leverage intelligence, choosing the right automation tool becomes critical. Zapier excels at quick, no-code automations with a
LangGraph and AutoGen are two prominent frameworks for developing AI agents, each catering to distinct needs in the realm of large language model (LLM) applications. Understanding their differences can help determine which aligns best with your project's requirements. What is LangGraph? LangGraph is an open-source framework built on
Executive Summary * AI at production scale: Magid's Collaborator suite now handles thousands of newsroom stories/day with all agents orchestrated on PromptLayer. * Efficiency gains: Early stations report 2–6 FTEs of capacity unlocked per newsroom; half of their top-read web stories become AI-assisted. * Rapid adoption: 8/10 journalists
Evaluating large language models requires careful measurement, consistency, and actionable results. With a growing number of frameworks available, it’s important to focus on tools that offer clear metrics, flexible integration, and support reliable model assessment. This article highlights the top five LLM evaluation tools, helping you choose solutions that
DeepSeek V3 and R1 represent two distinct approaches in the open-source large language model field. V3 relies on a mixture-of-experts architecture to deliver strong multitask performance, while R1 is designed for logical reasoning and efficient problem solving. This article compares their strengths, features, and user experiences to help you determine