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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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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.

Claude 3.5 Sonnet June Version vs GPT-4o

Whether you're a developer, researcher, or business leader, understanding how these models compare across key dimensions, like reasoning, speed, code generation, and multimodal capabilities, can help you choose the best tool for your needs. Reasoning & Knowledge Performance Claude 3.5 Sonnet shows a consistent edge in complex

GPT-OSS: First Impressions of OpenAI's New Open Weight Models

OpenAI just dropped two fully open LLMs: game-changer or nothing burger? After years of closed models, OpenAI has stunned the AI community by releasing the open-source models GPT-OSS 120B and GPT-OSS 20B under a fully permissive Apache 2.0 license. These are the company's first open-weight language models

Learnings from the Google Prompt Engineering Paper and others

The gap between basic and expert prompt engineering is smaller than you think. While most prompt engineers plateau after mastering basic techniques like "be specific" and "provide examples," the real breakthroughs come from understanding the nuanced capabilities of each model family. After extensive research into official

Claude Code vs. Cursor:

AI coding assistants have revolutionized how developers write and maintain code. Two standout tools take completely different approaches: Claude Code operates as an autonomous terminal agent that can handle complex, multi-step tasks independently, while Cursor enhances the familiar VS Code experience with real-time AI assistance. Understanding their core differences will

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