Building Agents with Claude Code's SDK

Run Claude Code in headless mode. Use it to build agents that can grep, edit files, and run tests. The Claude Code SDK exposes the same agentic harness that powers Claude Code—Anthropic's AI coding assistant that runs in your terminal. This SDK transforms how developers build AI

How OpenAI Codex Works Behind-the-Scenes (and How It Compares to Claude Code)

Suddenly everyone is switching from Claude Code to OpenAI Codex. I'm not sure which is better (I use both). But it's not just the model. They are made in different ways. The agentic architecture Codex uses will help us understand when to use it, and how

Claude Code has changed how we do engineering

Prioritization is different. Our company has shipped way faster in the last two months. Multiple customers noticed. It helped us build a “Just Do It” culture and kill prioritization paralysis. Claude Code (or OpenAI Codex, Cursor Agents) is an AI coding tool that is so good it made us rethink

Claude Code: Behind-the-scenes of the master agent loop

When Tom's Guide reported that Anthropic had to add weekly limits after users ran Claude Code 24/7, it caused quite a stir. Claude Code did something right. Let's dive into the architecture behind the scenes and see if we can learn a thing or two

LLM Idioms

An LLM idiom is a pattern or format that models understand implicitly - things their neural nets have built logic and world models around, without needing explanation. These are the native languages of AI systems. To me, this is one of the most important concepts in prompt engineering. I don&

Is JSON Prompting a Good Strategy?

A clever trick has circulated on Twitter for prompt engineering called "JSON Prompting". Instead of feeding in natural language text blobs to LLMs and hoping they understand it, this strategy calls to send your query as a structured JSON. For example... rather than "Summarize the customer feedback

How I Automated Our Monthly Product Updates with Claude Code

From tedious manual work to comprehensive automated analysis in one afternoon 0:00 /2:41 1× If you're like me, you probably dread writing those monthly product update emails. You know the ones – where you have to comb through dozens (or hundreds) of commits across multiple repositories, trying

HumanLoop Shutdown: Guide to Migrating Your Prompts and Evals to PromptLayer

HumanLoop is shutting down on September 8, 2025. If you're among the many teams who relied on HumanLoop for prompt management, evaluations, and observability, you need to act fast. The good news? PromptLayer offers everything HumanLoop did—and more. What is PromptLayer? PromptLayer is a comprehensive prompt engineering

Why LLMs Get Distracted and How to Write Shorter Prompts

Context Rot: How modern LLMs quietly degrade with longer prompts — and what you can do about it Context Rot: What Every Developer Needs to Know About LLM Long-Context Performance How modern LLMs quietly degrade with longer prompts — and what you can do about it If you've been stuffing

The Agentic System Design Interview: How to evaluate AI Engineers

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,

What is Context Engineering?

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

Best Practices for Evaluating Back-and-Forth Conversational AI

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

How a SaaS Unicorn Uses PromptLayer to Send Millions of Hyper-Personalized Emails at $0.002 Each

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

Automating 100,000+ Hyper-Personalized Outreach Emails with PromptLayer

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

Swapping out Determinism for Assumption-Guided UX

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

Lawyers in the Loop: How Midpage Uses PromptLayer to Evaluate and Fine-Tune Legal AI Models

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

How NoRedInk Used PromptLayer Evals to Deliver 1M+ Trustworthy Student Grades

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

Top 5 AI Dev Tools Compared: Features and Best Use Cases

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

Top 5 No Code LLM AI Tools for Building LLM Applications

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

2025 State of AI Engineering Survey: Key Insights from the AI Engineer World Fair

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

Production Traffic Is the Key to Prompt Engineering

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

How Magid built enterprise-grade AI agents for content creation with PromptLayer

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

AI Sales Engineering: How We Built Hyper-Personalized Email Campaigns at PromptLayer

TL;DR Our AI sales system automates hyper-personalized email campaigns by researching leads, scoring their fit, drafting tailored four-email sequences, and integrating seamlessly with HubSpot. With this approach, we achieve: * ~7% positive reply rate, resulting in way more meetings than we can handle * 50–60% email open rates The key

Using AI as a Content Writer: How Not to Get Left Behind

There are two schools of thought emerging in the AI space right now. The first is pessimistic: "AI is coming, it's going to be our new God, everything's going to be gone, we're not going to be able to work anymore." The

How to Evaluate LLM Prompts Beyond Simple Use Cases

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

Where to Build AI Agents: n8n vs. PromptLayer

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

Building Better AI Systems: Lessons from Anthropic's AI Engineer Talk

"Evals are your company's intellectual property" - Alexander Bricken at AI Engineer Summit I recently attended Anthropic's talk at AI Engineer Summit, and it offered fascinating insights into how one of the leading AI companies thinks about building robust AI systems. Here are my

Lessons from OpenAI's Model Spec

OpenAI's Model Spec tells us a lot about how the company thinks about prompt engineering. Let's explore it and see how to use it in your daily prompting. The Three-Layer Approach The Model Spec uses three layers: objectives, rules, and defaults. This structure makes prompts more

The Death of Prompt Engineering Has Been Greatly Exaggerated

As AI models become increasingly sophisticated, there's a growing narrative that prompt engineering – the art and science of instructing large language models – will soon become obsolete. As models get better at understanding natural language, will the need for carefully crafted prompts will disappear? The death of prompt engineering

PromptLayer Announces our $4.8M Seed Round

Software development is being fundamentally reshaped by AI, but the biggest challenge isn't technical expertise – it's domain knowledge. The next generation of AI products will be built by doctors, lawyers, and educators, not just machine learning engineers. We're excited to announce that PromptLayer has

The Future of AI is for Subject Matter Experts, Not ML Engineers

Here's my take on where AI is headed and why subject matter experts, not ML engineers, will be the ones building the next generation of AI applications. Most of this comes from the podcast above. The Big Picture One of the main predictions I base a lot of

What does Deepseek mean for Open Source Models and American innovation?

My technically curious dad just asked if Deepseek is a game changer. It's a fair question that gets at the heart of what's happening in AI right now. What's Really Going On Deepseek is good. They've built the best open-source model out

Is "Reasoning" Just Another API Call?

What we can learn from o1 models and "Thinking Claude" The AI landscape has shifted dramatically. We now have access to both "smart" and "dumb" models, where smart model families o1 take time to think and reason before answering. But here's where

Turn a Typeform into an AI Intake Agent: Pt 1 — Prompts and Evals

Introduction Every day, organizations collect data through forms. Employee onboarding, customer intake, surveys - they all present users with walls of input fields and validation messages. It's tedious, impersonal, and often frustrating. But what if we could make this experience feel more natural? What if filling out forms

Is RAG Dead? The Rise of Cache-Augmented Generation

As language models evolve, their context windows keep getting longer and longer. This evolution is challenging our assumptions about how we should feed information to these models. Enter Cache-Augmented Generation (CAG), a new approach that's making waves in the AI community. What is CAG? Cache-Augmented Generation loads all

Unlocking the Human Tone in AI

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,

Your AI Might Be Overthinking: A Guide to Better Prompting

Recent research suggests that modern AI language models, particularly reasoning-focused LLMs like o1, often engage in excessive computation. Here's what this means for prompt engineering and how you can optimize your AI interactions. The Overthinking Problem Consider this striking example: when asked to solve a simple "2+

How OpenAI's o1 model works behind-the-scenes & what we can learn from it

The o1 model family, developed by OpenAI, represents a significant advancement in AI reasoning capabilities. These models are specifically designed to excel at complex problem-solving tasks, from mathematical reasoning to coding challenges. What makes o1 particularly interesting is its ability to break down problems systematically and explore multiple solution paths—

All you need to know about prompt engineering

I recently recorded a podcast with Dan Shipper on Every. We covered a lot, but most interestingly spoke a lot about prompt engineering from first principles. Figured I would out all the highlights in blog form. The reports of prompt engineering's demise have been greatly exaggerated. The Three

The Prompt Engineering Triangle – the Future of GenAI

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

Prompt Engineering Guide to Summarization

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

Understanding prompt engineering

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

A How-To Guide On Fine-Tuning

Fine-tuning is an extremely powerful prompt engineering technique. This how-to guide will show you exactly how to do it effectively.

Prompt Templates with Jinja2

Jinja2 is a powerful templating engine that can take your prompts to the next level. See how it’s more powerful than just f-string.

Comparing Tool Calling in LLM Models

The Importance of Tool Calling

Prompt Engineering with Anthropic Claude

Tips on how to prompt Claude more effectively. Take-aways from a talk by Anthropic’s “Prompt Doctor” (Zack Witten).

You should be A/B testing your prompts.

Ground truth is subjective, and the only reliable way to evaluate prompts is with real user metrics. A/B testing helps you safely iterate.

Tool Calling with LLMs: How and when to use it?

LLM tool calling as an AI idiom, its benefits over JSON mode, and examples of how to use function calling in your real projects.

Why Fine-Tuning is (Probably) Not for You

For some reason, it feels like every startup now has its own custom-trained model. This is probably not a good idea.

Speeding up iteration with PromptLayer’s CMS (tips for prompt management)

This post was cross-posted with permission from Greg Baugues. You can find the original at https://www.haihai.ai/friction/

Gorgias Uses PromptLayer to Automate Customer Support at Scale

Gorgias uses PromptLayer every day to store and version control prompts, run evals on regression and backtest datasets, and review logs.

From Zero to 1.5 Million Requests: How PromptLayer Powered Meticulate’s Viral Launch

Meticulate Case Study — PromptLayer empowers AI startup to debug complex agent LLM pipelines, rapidly build MVP, and go viral.

How Speak Empowers Non-Technical Teams with Prompt Engineering and PromptLayer

Speak Case Study — PromptLayer empowered content, product & bizops teams to efficiently scale AI-driven workflows, fueling rapid growth.

How Ellipsis uses PromptLayer to Debug LLM Agents

Ellipsis Case Study — PromptLayer slashes LLM agent debugging time by 75%, fueling 500K+ requests and 30 new customers in just 6 months.

How PromptLayer Enables Non-Technical Prompt Engineering at ParentLab

ParentLab Case Study — How non-technical prompt engineers use PromptLayer to build highly-personalized AI user interactions.

Upgrading to GPT-4o: What You Need to Know

OpenAI released gpt-4o two days ago, their new flagship model. The big question now is: Should you upgrade?

Prompt Routers and Modular Prompt Architecture

Prompt routers are the alternative to using monolithic, master prompts. They are faster, cheaper, and way easier to maintain.

What can we learn from ChatGPT jailbreaks?

Learning to prompt engineer through malicious examples.

Building your own ChatGPT, the right way

You probably know what ChatGPT is already. The first smash success of the LLM revolution.

Migrating prompts to open-source models

In honor of adding Mistral support to PromptLayer this week, we will evaluate & migrate our current prompts to open-source models.

Our Favorite Prompts from the Tournament

PromptLayer recently hosted one of the first-ever prompt engineering tournaments. Here are our favorite prompt submissions.

Scalable Prompt Management and Collaboration

Prompts are the magic that makes your LLM system work. They are your secret sauce 🥫 Make sure they are organized & versioned in a CMS.