Prompt Engineering For Developers: A Guide To Practical Patterns
Prompt engineering for developers is the practice of turning a software task into clear, testable instructions that an AI model can use to produce code, debugging analysis, QA plan...
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Prompt engineering for developers is the practice of turning a software task into clear, testable instructions that an AI model can use to produce code, debugging analysis, QA plan...
Как сделать изменения, вносимые с помощью LLM, управляемыми, проверяемыми и воспроизводимыми.Программные ассистенты LLM продемонстрировали значительную ценность, но в основном для...
Most LLM failures in production aren’t random — they’re predictable. I kept hitting broken JSON, silent failures, and outages that froze my entire app. Prompt engineering didn’t fi...
不是 prompt 技巧,是結構 AI 生圖社群裡有大量的「prompt 技巧」——加什麼關鍵字畫面會更好、用什麼形容詞光線會更美。這類文章很多,也很有用。 但我們的問題不一樣。 我們有一個劇組。十...
Look at any builder's prompt history and you'll see a collection of highly specific, sometimes chaotic, one-off prompts. We use AI to debug a single error message, refactor a messy...
TL;DR: Compounding Systems and Agents Go Hand-in-Hand Every AI conversation starts from zero because the model forgets who you are. The Claude Cowork Online Course teaches you to c...
System prompts form the foundation of generative AI applications. A system prompt is a collection of instructions and operational context provided to a large language model (LLM) t...
Comments
Prompt engineering is what you learn first. Context engineering is what you need when you're actually trying to ship something. Here's the distinction that took me too long to und...
I started treating AI image prompts more like small test cases, and the results became much easier to understand. Before that, my workflow was messy. I would write one long reques...
If you build LLM agents, you probably write the same prompt more than once. A Markdown config for one tool, a rules file for another, and then the actual [{role, content}] messages...
A multi-SLM platform creates value only when specialization does not introduce a new latency tier. Small language models are inexpensive enough to dedicate to focused work such as ...
I've found a core misconception is persistent... people use the CLIP interrogator model expecting it to recover the original prompt from an image. It cannot do this, and if you loo...
When building applications, I always build the core first, then the interfaces. It was no different with Ask the Canon: a uv run main.py ask "..." CLI for quick iteration and valid...
Originally appeared on avdi.codes. Users don’t experience systems as implementations. They experience them as continuity. leaflet.pub: UI Is a Conservation Layer
As enterprise generative AI transitions from simple, conversational chatbots to autonomous multi-agent workflows, developers face a critical bottleneck: scale. In a production envi...
Where the Problem Sits Everyone talks about model safety. Not enough people talk about what happens when the input itself is the weapon. Prompt injection is not a niche edge case....
Originally appeared on dmitrytsepelev.dev.Like it or not, a lot of applications are adding AI–native features: anything related to automated answers, object classification, knowled...
LLMs are becoming part of everything. They read web pages, summarize PDFs, inspect emails, process customer tickets, call tools, write code, and sometimes even make decisions insi...
Enterprise Document Intelligence [Vol.1 #8B] - A fixed BASE, the rules each question needs, one registry: the dispatcher that turns a parsed question into a typed LLM call The post...
A few weeks ago, I read a line from Boris Cherny, the person behind Claude Code, that stuck with me. He said he does not prompt Claude anymore. He has loops running, and those loop...
LLMs don’t fail because they forget—they fail because they remember too much. As conversations grow, prompts accumulate redundant and low-value tokens, driving up cost and latency...
Most people start using Claude Code the same way they use ChatGPT: they open Claude, submit a prompt describing the task, and expect a…Continue reading on UX Planet »
In this tutorial, we use GEPA as a reflective prompt-evolution framework to improve how a small language model solves multi-step arithmetic word problems. We start from a weak seed...
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