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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

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

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What we can learn from Anthropic's System prompt updates

Claude's system prompts evolved through dozens of versions in 2024-2025. Each change reveals concrete lessons about 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 post extracts the patterns

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

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

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

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

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

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

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

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 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

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

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

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.

DeepSeek R1 vs OpenAI O1: An In-Depth Comparison

OpenAI and DeepSeek are emerging as dominant players in the development of advanced language models, each bringing distinct strengths to the table. Their latest models, OpenAI's O1 and DeepSeek's R1, represent significant strides in AI reasoning and problem-solving. While OpenAI's O1 is engineered for

What is Test Time Compute?

“More compute!” is a common refrain these days in discussions about enhanced LLM performance and capability. Just scan a few recent headlines to see the resources major companies are willing to pour into getting it. In a broad sense, this refers to more and better hardware used in model training.

What is In-Context Learning? How LLMs Learn From ICL Examples

One of the key factors driving the growth of Large Language Models (LLMs) is in-context learning (ICL), a unique learning paradigm that allows LLMs to adapt to new tasks by processing examples provided directly within the input prompt. This article breaks down the intricacies of ICL, exploring its mechanisms, benefits,

Best Tools to Measure LLM Observability

Large language models (LLMs) are revolutionizing how we interact with technology, but their complexity introduces unique challenges for developers. Ensuring LLMs perform reliably and efficiently requires robust observability—the ability to understand and diagnose their behavior. This article compares the best tools for measuring LLM observability, examining their key features

Top Tools for AI Evals

As AI systems become more sophisticated and integrated into our daily lives, the need for robust, reliable, and comprehensive evaluation tools becomes increasingly critical. AI evaluation tools enable developers, researchers, and organizations to assess the performance, identify weaknesses, and ensure the ethical and responsible deployment of AI models. This article

DeepSeek V2 vs. Coder V2: A Comparative Analysis

While both DeepSeek V2 and Coder V2 leverage DeepSeek's innovative Mixture-of-Experts (MoE) architecture, DeepSeek V2 is a versatile, general-purpose language model excelling in both natural language processing and code generation, whereas Coder V2 is specifically designed and optimized for a wide array of coding tasks Table of Contents

OpenAI o3 vs DeepSeek r1: An Analysis of Reasoning Models

OpenAI's upcoming o3 and DeepSeek's r1 represent significant advancements in the domain of reasoning models. Both models have garnered attention for their impressive performance on various benchmarks, sparking discussions about the future of AI and its potential impact on various industries. From what we know, OpenAI&

Mega Blog | Everything About DeepSeek R1

DeepSeek, a new Chinese AI company that released r1, is turning heads and this is a comprehensive blog to learn about its implications. Founded in late 2023 by Liang Wenfeng—a serial entrepreneur who also runs the hedge fund High-Flyer—DeepSeek is now a major AI player. Its models are

What Are Prompt Evaluations?

What makes one prompt more effective than another? And how can we quantify and document those differences? That's where prompt evaluations come in. Prompt evaluations are a way for you to assess and refine the inputs (prompts) you provide to an AI model, resulting in improved performance. Whether

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