BrainTrust Alternatives - The Best Prompt Management Platforms in May 2026

Introduction If you’re evaluating Braintrust, you’re probably not “just browsing” - you’re already thinking about the operational reality: tracing volume, evaluation cost, and how quickly your team can ship changes. Braintrust’s public pricing is transparent about core drivers like trace spans, processed data/storage, scores, and retention,

The Antidote is Soul

How can AI teams stand out in the age of AI agents? Every website has cool animations now. Every SaaS landing page has the same purple gradients, the same floating illustrations, the same polished corners. AI made perfection free. Every digital meal is a bowl. We live in the age

We hosted the first Vibe Coding Olympics

Last week, we hosted the first-ever Vibe Coding Olympics in the heart of New York City: a three-round, aggressively time-boxed hackathon where the deciding score was whether what teams shipped felt good to use. Prompting used to be the hard part. Now the hard part is deciding

The emergence of Agent-First Software Design

There's a shift happening in how we build software. For decades, programming meant writing explicit if/else decision trees. Parse this response. Handle this edge case. Chain these steps together. But a new paradigm is emerging where the job of the software engineer isn't to write

Get Out of the Model's Way

When something doesn't work, the instinct is to add more. More guardrails. More tools. More structure. With LLMs, this instinct is often wrong. Paradoxically, AI engineers are building elaborate systems to constrain models that are now smarter than the constraints themselves. We're doing the model'

Watch my AI Engineering talk: How Claude Code Works

A few weeks ago, I gave a talk at the legendary AI Engineering Summit. It was titled: “How Claude Code Works” Claude Code completely changed how our engineering org functions. It really feels like a “moment” in this space. Importantly, it represents a new standard for building autonomous agents. Suddenly

Every agent should be a VM

There is no doubt that OpenAI's Codex CLI and Anthropic's Claude Code agents are order of magnitude shifts in what we can expect from coding agents. I recently did a deep dive and wrote articles exploring how Claude Code works and how OpenAI Codex works behind

Bringing the Fundamentals to AI Engineering

AI engineering is a new discipline, but that doesn't mean we should throw out everything we know about engineering. The same fundamentals apply: de-scope ruthlessly, think in functions, and don't build what you don't need. Too many are skipping the fundamentals. Marketing Outpaced

Black Box Prompt Engineering: Why Not Knowing How It Works Is Actually the Point

I recently sat down with Stewart Alsop III on the Crazy Wisdom Podcast to talk about PromptLayer, AI engineering, and why the shift from deterministic software to probabilistic LLM systems is changing how teams design, evaluate, and ship applications. As prompts, agents, and AI workflows move deeper into production, many

How OpenAI's Deep Research Works

OpenAI's Deep Research is designed to accomplish in about 30 minutes what can take human researchers 6–8 hours—using a specialized reasoning model to autonomously browse the web, read dozens of sources, and produce cited reports. Deep Research represents a new category of agentic AI that doesn&

What we can learn from Anthropic's System prompt updates

Claude's system prompts evolved through dozens of versions in 2024–2025, with each change revealing concrete lessons for production prompt engineering. Find all their system prompts here https://docs.claude.com/en/release-notes/system-prompts Let's read them and see what we can learn! This

AI doesn't kill prod. You do.

I had a conversation with a customer yesterday about how we use AI coding tools. We treat AI tools like they're special, and something to be scared of. Guardrails! Enterprise teams won't try the best coding tools because they are scared of what might happen. AI

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 feels different. Our company has shipped much faster over the last two months, and multiple customers noticed. It helped us build a “Just Do It” culture and cut through prioritization paralysis. Claude Code (or OpenAI Codex, Cursor Agents) is an AI coding tool that is so good it made

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 tend to recognize implicitly — conventions their training has reinforced and their internal representations can use without extra explanation. These are the native languages of AI systems. To me, this is one of the most important concepts in prompt engineering. I

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 shut down on September 8, 2025. If your team relied on HumanLoop for prompt management, evaluations, and observability, you may still be looking for a stable replacement. PromptLayer offers everything HumanLoop did—and more. What is PromptLayer? PromptLayer is a comprehensive prompt engineering platform that serves as the “Git

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

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,

What is Context Engineering?

The term "prompt engineering" surged after ChatGPT launched in late 2022. It began as a practical toolkit for getting better responses from AI: be explicit, add examples, write role-playing instructions, and experiment with the prompt optimization patterns many teams reached for first. As I've written

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, its adaptive writing platform serves 60% of U.S. school districts and millions of students worldwide. But when the team set out to build an AI grading assistant, they faced a challenge many EdTech

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

Artificial intelligence continues to transform software development, influencing how code is written, tested, deployed, and maintained. Developers evaluating the top AI dev tools in 2025 and beyond will find a diverse set of solutions designed to streamline workflows, support creativity, and help solve complex problems. This article explores the leading

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

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 highlights the rapid pace of model and prompt iteration, persistent challenges around

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

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

TL;DR for AI teams building and shipping LLM apps 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

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 practical insight into how one of the leading AI companies approaches building more reliable AI systems. Here are my key

Lessons from OpenAI's Model Spec

OpenAI's Model Spec is a useful reference for how the company describes model behavior, instruction hierarchy, and prompt-engineering tradeoffs. Here's what it means for AI teams building LLM-powered apps, prompts, and agents—and how to apply it in everyday prompting. The Three-Layer Approach

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 often isn't technical expertise—it's domain knowledge. The next generation of AI products will be built with doctors, lawyers, educators, and other subject-matter experts working alongside AI engineers, not just machine learning specialists.

Is RAG Dead? The Rise of Cache-Augmented Generation

As language models evolve, their context windows keep getting longer—and AI teams are rethinking how much information to include up front versus retrieve on demand at inference time. This shift is challenging assumptions about retrieval, latency, cost, and prompt design. Enter Cache-Augmented Generation (CAG), an approach gaining attention

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

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, marked a major step forward in AI reasoning capabilities. These models are built for complex problem-solving tasks, from mathematical reasoning to coding challenges, where deliberate analysis and accuracy matter. For AI teams, o1 is especially useful when applications need to break down

All you need to know about prompt engineering

I recently recorded a podcast with Dan Shipper on Every. We covered a lot of ground, but the most useful thread was 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

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 remains one of the most practical ways to use language models in production. But creating a truly useful summarization agent goes far beyond a simple "summarize this" command. In this guide, we’ll explore advanced prompt engineering techniques that help summarization agents stay reliable, source-

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

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).

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

Many startups often begin with custom-trained AI models, which might not always be the most strategic approach.

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 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.

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.