# prompt engineering Published articles for prompt engineering. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The generative AI customization spectrum: From prompt engineering to custom models on AWS DevFeed: [The generative AI customization spectrum: From prompt engineering to custom models on AWS]() Original publisher: [Read original article]() Author: Bhavya Sruthi Sode Published: 2026-09-14T15:47:12Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Generative AI](), [Amazon Web Services](), [Prompt Engineering](), [Fine-tuning](), [Retrieval Augmented Generation (RAG)](), [Amazon Bedrock](), [foundation-models](), [Anthropic Claude](), [Nova](), [llama]() Tags: [advanced-300](), [ai](), [amazon-bedrock](), [anthropic-claude](), [aws](), [best-practices](), [chain-of-thought](), [fine-tuning](), [foundation-models](), [generative-ai](), [llama](), [nova](), [optimization](), [prompt-engineering](), [rag]() ### AI overview This AWS article presents an eight-step decision framework for customizing generative AI workloads. It compares progressively more involved approaches, including prompt engineering, Retrieval Augmented Generation (RAG), fine-tuning, continued pre-training, and custom models such as Amazon Nova Forge, emphasizing that teams should start with the simplest approach and escalate when greater control or domain specificity is required. ### Source excerpt Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simple and escalate only when you must. ## ToolGrad: Efficient tool-use dataset generation with textual "gradients" DevFeed: [ToolGrad: Efficient tool-use dataset generation with textual "gradients"]() Original publisher: [Read original article]() Published: 2026-09-10T22:50:22Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [dataset](), [Large Language Model](), [LLM Techniques](), [Machine learning](), [Fine-tuning](), [LLM evaluation / benchmarking]() Tags: [agent](), [cost](), [datasets](), [evaluation](), [fine-tuning](), [generation](), [llm](), [machine-intelligence](), [machine-learning](), [natural-language-processing](), [prompt-engineering](), [tool]() ### AI overview ToolGrad generates tool-use chains before deriving corresponding user queries, aiming to create complex training data for LLM tool use more efficiently and at lower cost than exploration-based approaches. ### Source excerpt Machine Intelligence ## How we make AI coding more cost efficient without sacrificing task quality DevFeed: [How we make AI coding more cost efficient without sacrificing task quality]() Original publisher: [Read original article]() Author: Erik Kristensen Published: 2026-09-02T18:00:00Z Content type: article Language: en Sources: [GitHub](), [GitHub Engineering]() Topics: [GitHub Copilot](), [AI-assisted coding](), [GitHub Copilot CLI](), [coding](), [GitHub Copilot app](), [GitHub Copilot code review]() Tags: [agentic-ai](), [agentic-coding](), [ai](), [ai-coding](), [ai-ml](), [architecture-optimization](), [benchmarks](), [compression](), [cost](), [engineering](), [experiments](), [github-copilot](), [github-copilot-cli](), [github-copilot-code-review](), [llms](), [prompt-engineering]() ### AI overview GitHub Copilot's efficiency work focuses on total task cost and duration rather than minimizing tokens in individual tool responses. The article describes evaluating changes with coding benchmarks and controlled experiments, and explains that overly compressed output can cause agents to repeat work. ### Source excerpt Why shorter outputs can cost more, and how GitHub Copilot reduces wasted work across the complete coding task. The post How we make AI coding more cost efficient without sacrificing task quality appeared first on The GitHub Blog. ## Claude's new system prompt really doesn't want to reproduce song lyrics DevFeed: [Claude's new system prompt really doesn't want to reproduce song lyrics]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-02T14:16:42Z Content type: opinion Language: en Sources: [Simon Willison]() Topics: [Claude](), [anthropic](), [Fable]() Tags: [ai](), [ai-2-235](), [ai-ethics](), [ai-ethics-342](), [anthropic](), [claude](), [claude-310](), [generative-ai](), [generative-ai-1-981](), [git-scraping](), [git-scraping-38](), [llms](), [llms-1-947](), [prompt-engineering](), [prompt-engineering-197](), [system-prompts](), [system-prompts-57](), [updated]() ### AI overview The article examines changes in Anthropic's published Claude consumer system prompts, focusing on new restrictions against reproducing song lyrics and generating copyrighted characters or logos. It also discusses changes to Claude's response style and conversation-ending guidance. ### Source excerpt Anthropic publish the system prompts for their Claude consumer applications (Claude.ai and the Claude mobile apps - sadly not for Claude Cowork or Claude Code). I love that they do this, and that they share not just the current prompts but historic changes to their prompts as well. They used to keep all of the prompts on a single page, but when I checked today I noticed they had re-arranged those prompts into an index page and then a page per model - here's the page for Haiku 4.5 for example, which has the original prompt from October 15th 2025 and an updated prompt from January 18th 2026. A neat thing about Anthropic's platform.claude.com/docs site is that it's designed to be usable by LLMs. You can add .md to any page to get back the content as Markdown - here's the system prompt index page and the Markdown prompts for Fable 5.1. TL;DR: this makes it really easy to diff the prompts. Don't reproduce song lyrics Don't draw copyrighted characters or logos Tweaks to Claude's answering style The missing end_conversation guidelines Recommended substance support sites Reliable cutoff date of June 2026 How I'm tracking these prompts Don't reproduce song lyrics Let's start with the most interesting difference between Fable 5 and Fable 5.1: There's a hefty new section about not reproducing song lyrics: Claude does not reproduce song lyrics, poems, or passages from books and articles, in whole or in part -- including the last lines, a chorus or hook, a melody written out note by note, or lines the person pastes in one at a time and describes as their own song. Once Claude has declined such a request in a conversation, it keeps declining narrower or reworded versions of it for the rest of that conversation, and offers to describe or analyze the work instead. Song lyrics and poems first published before 1929 are fine -- a Shakespeare sonnet, a Keats ode, the Italian libretto of a Puccini aria -- but Claude goes by what it knows of the work's date rather than the person's say-so, ## Artificial Intelligence: Glossary DevFeed: [Artificial Intelligence: Glossary]() Original publisher: [Read original article]() Author: Caleb Sponheim Published: 2026-08-21T17:00:00Z Content type: article Language: en Sources: [NN/g latest articles and announcements]() Topics: [Artificial Intelligence](), [AI Agent](), [AI Development](), [Algorithms](), [Prompt Engineering]() Tags: [agentic-ai](), [agents](), [ai-agent](), [ai-definitions](), [ai-evals](), [ai-glossary](), [ai-glossary-for-ux](), [ai-hallucination](), [ai-terminology](), [ai-terminology-for-product-teams](), [ai-terms](), [ai-terms-for-designers](), [ai-vocabulary](), [article](), [artificial-intelligence](), [artificial-intelligence-glossary](), [context-window](), [genai-glossary](), [generative-ai](), [generative-ui](), [glossary](), [knowledge-cutoff](), [large-language-model](), [machine-learning](), [model-context-protocol](), [prompt-engineering](), [prompt-injection](), [rag](), [tokens](), [ux](), [vibe-coding]() ### AI overview A plain-language glossary of artificial-intelligence terminology used in products and UX work. It explains concepts including agents, agentic systems, AI development, algorithms, AI-generated content, and AI-related claims, while noting that terminology can vary among vendors and researchers. ### Source excerpt Plain-language definitions of the AI terms that come up in product and design work, from tokens and context windows to agents, evals, and prompt injection. ## Agentic Engineering 101 DevFeed: [Agentic Engineering 101]() Original publisher: [Read original article]() Author: Daniel Moka Published: 2026-08-12T05:01:48Z Content type: tutorial Language: en Sources: [Craft Better Software]() Topics: [Loop Engineering](), [Prompt Engineering](), [context](), [Artificial Intelligence]() Tags: [agent](), [agentic](), [agentic-engineering](), [agents](), [ai](), [context-engineering](), [engineering](), [prompt](), [prompt-engineering]() ### AI overview This tutorial introduces agentic engineering as a set of layers around an AI model. It describes prompt engineering, context engineering, harness engineering, loop engineering, graph engineering, and memory engineering, with detailed guidance in the supplied text on prompt design and context curation. ### Source excerpt Prompt vs Context vs Harness vs Loop vs Graph Engineering ## AI Native Software Development: 5 Radical Team Decisions Now DevFeed: [AI Native Software Development: 5 Radical Team Decisions Now]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-08-10T11:33:22Z Content type: opinion Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Artificial Intelligence](), [AI Development](), [ai-coding](), [Claude Code](), [cursor](), [GitHub Copilot](), [Terminal](), [Command-line interface](), [Prompt Engineering](), [sdlc](), [FIRST]() Tags: [ai](), [ai-coding](), [claude-code](), [coinbase](), [copilot](), [cursor](), [prompt-engineering](), [software-development](), [terminal](), [workflow]() ### AI overview The article argues that AI-native software development requires redesigning an organization around AI-assisted coding rather than merely purchasing tools. It introduces five team decisions, including no-IDE weeks, AI-focused hiring, platform engineering, and an agentic software development lifecycle; the supplied text details the no-IDE experiment and begins the discussion of measuring AI usage through leaderboards and velocity. ### Source excerpt The 5 team decisions that make you AI native software development: no-IDE weeks, AI hiring, platform engineers, agentic SDLC. ## The Real Python Podcast - Episode #306: Programmatically Developing LLM Prompts With DSPy DevFeed: [The Real Python Podcast - Episode #306: Programmatically Developing LLM Prompts With DSPy]() Original publisher: [Read original article]() Author: Real Python Published: 2026-08-07T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [DSPy](), [Large Language Model](), [Prompt Engineering](), [Retrieval Augmented Generation (RAG)](), [Python](), [llamaindex]() Tags: [dspy](), [llamaindex](), [llm](), [podcast](), [programming](), [prompt-engineering](), [python](), [rag]() ### AI overview This podcast episode explains how DSPy programmatically develops and optimizes prompts for LLM applications. It covers declarative signatures, prompt compilation and tuning, and the limitations of manual prompt engineering. The episode also highlights a course on using LlamaIndex for RAG in Python. ### Source excerpt How can you move from manually writing prompts for an LLM application toward defining them programmatically? This week on the show, Brett Kennedy returns to discuss his new book "Building LLM Applications with DSPy." ## How direct, context-rich prompts shape technical LLM conversations DevFeed: [How direct, context-rich prompts shape technical LLM conversations]() Original publisher: [Read original article]() Published: 2026-08-06T00:00:00Z Content type: opinion Language: en Sources: [Mutt0-ds Notes]() Topics: [Prompt Engineering](), [ChatGPT](), [Large Language Model](), [Artificial Intelligence](), [math]() Tags: [ai](), [chatgpt](), [deep-dive](), [llm](), [prompt-engineering](), [technical]() ### AI overview This opinion article examines Terence Tao's ChatGPT conversation about verifying a counterexample to the Jacobian conjecture. It argues that providing informed context and precise technical constraints can guide LLMs more effectively than elaborate prompt templates. ### Source excerpt There is something so satisfying in reading Terence Tao's ChatGPT conversation, where he and the model together verify a counterexample to... ## One-shotting a Raccoon Heist game using Claude Fable 5 DevFeed: [One-shotting a Raccoon Heist game using Claude Fable 5]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-08-05T19:42:38Z Content type: tutorial Language: en Sources: [Simon Willison]() Topics: [Claude](), [Claude Code](), [GitHub Pages](), [GitHub](), [Code]() Tags: [ai](), [ai-2-235](), [anthropic](), [anthropic-336](), [browser](), [claude](), [claude-310](), [claude-code](), [claude-mythos-fable](), [claude-mythos-fable-41](), [code](), [coding-agents](), [coding-agents-248](), [fable](), [game-design](), [game-design-22](), [generative-ai](), [generative-ai-1-981](), [github](), [github-pages](), [iphone](), [llms](), [llms-1-947](), [prompt-engineering](), [prompt-engineering-197](), [text-to-image](), [text-to-image-47](), [vibe-coding](), [vibe-coding-97]() ### AI overview The article describes an experiment in which Claude Fable 5, running through Claude Code for web, built a Raccoon Heist game from an older GPT-3 game concept and screenshots. It also explains using GitHub Pages to preview work from Claude Code for web and notes that published content from a private repository may be viewable by anyone who guesses the repository name. ### Source excerpt Back in 2022 I tweeted screenshots of a game concept generated by GPT-3 and some concept "art" created using DALL-E. Today, on the fourth anniversary of that tweet, I decided to see if Claude Fable 5 (running in Claude Code for web) could build the entire game from the content of that tweet. It did a pretty good job of it! You can play the game here. Here's the GitHub repo, and a short video demo: Your browser does not support HTML5 video. How I built this This is the August 5th, 2022 tweet: My GPT-3 prompt back then was: Write a detailed product description of a computer game where a team of raccoons go on heists. This was in the text completion days, where you asked GPT-3 to complete the rest of the text. It completed: In "Raccoon Heist", you and your team of thieving raccoons are tasked with pulling off a series of daring heists. From robbing banks to stealing priceless art, no job is too big or too small for your furry crew. You'll need to use your wits and your skills to avoid the police and make a clean getaway with the loot. With exciting gameplay and a charming cast of characters, "Raccoon Heist" is the perfect game for anyone looking for a light-hearted caper. My prompt to DALL-E was short: Screenshot from a video game where a team of raccoons go on a heist Today's experiment: can I dump those screenshots into Fable 5 with a prompt telling it to write a game, then leave it to its own devices and get a working game at the end? Setting Claude Code for web up to use GitHub Pages A frustrating thing about Claude Code for web is that it can be hard to test what it's working on while it's still working. I've been using GitHub Pages to work around that limitation, and found it to work really well. Here's my process: Create a new repository for the project at https://github.com/new - this can be public or private, the trick works equally well for both. Start a Claude Code for web session, in the Claude iPhone or Desktop apps or in the browser at https://claude.ai/c ## Build Azure Skills Faster with Cloud Academy, a Visual Studio Subscriber Benefit DevFeed: [Build Azure Skills Faster with Cloud Academy, a Visual Studio Subscriber Benefit]() Original publisher: [Read original article]() Author: James Rempt Published: 2026-08-04T14:00:38Z Content type: article Language: en Sources: [Visual Studio Blog]() Topics: [Azure](), [Visual Studio](), [Cloud](), [Artificial Intelligence](), [DevOps](), [Automation](), [workflow automation](), [Prompt Engineering]() Tags: [ai](), [automation](), [azure](), [cloud](), [devops](), [learn](), [learning](), [prompt-engineering](), [visual-studio](), [visual-studio-subscriptions]() ### AI overview This article presents Cloud Academy as a Visual Studio subscriber benefit for building practical Azure skills. It highlights hands-on labs, certification-focused learning paths, assessments, sandbox experimentation, and AI-powered guidance, with examples spanning Azure administration, deployment, DevOps, security, cost optimization, Azure AI, and prompt engineering. ### Source excerpt If you've been looking for a practical way to build Azure skills, Cloud Academy (a QA company) gives Visual Studio subscribers hands-on learning that fits into busy schedules. Whether you're new to Azure, expanding your cloud expertise, working towards certifications or preparing for more advanced projects, this benefit helps you learn by doing. What you'll find in Cloud Academy The Cloud Academy benefit gives Visual Studio subscribers a flexible [...] The post Build Azure Skills Faster with Cloud Academy, a Visual Studio Subscriber Benefit appeared first on Visual Studio Blog. ## Context engineering vs. prompt engineering DevFeed: [Context engineering vs. prompt engineering]() Original publisher: [Read original article]() Author: Trevor Garcia Published: 2026-07-30T10:36:12Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Prompt Engineering](), [AI Agent](), [Large Language Model](), [Model Context Protocol](), [sdlc](), [data]() Tags: [agent](), [agentic](), [agents](), [ai](), [ai-agent](), [data](), [development](), [engineering](), [llms](), [mcp](), [prompt-engineering](), [sdlc](), [workflows]() ### AI overview The article compares prompt engineering with context engineering for LLM-based AI agents. Prompt engineering defines where agents should find data, how they should use it, and how they should format responses, while context engineering adds historical data, memory, service relationships, and next-step guidance. The article argues that both approaches work together to improve agentic development and team workflows, with MCP servers providing access to the required context. ### Source excerpt Prompt engineering and context engineering may seem at odds, but their differences are more akin to the differences between 2D and 3D. ## Context engineering for AI: what it is & how to build it DevFeed: [Context engineering for AI: what it is & how to build it]() Original publisher: [Read original article]() Author: Simba Khadder Published: 2026-07-29T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [AI Chat](), [AI Bots]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [apps](), [crm](), [engineering](), [history](), [how-to](), [inference](), [infrastructure](), [latency](), [llm](), [model](), [policy](), [prompt-engineering](), [reasoning](), [support](), [systems](), [tech-de](), [tool]() ### AI overview A guide to context engineering for AI agents: selecting and managing the instructions, history, retrieved documents, tool outputs, and state sent to an LLM during inference. It explains how deliberate context management helps avoid hallucinations, excessive context, latency, and degraded reasoning in multi-step agent workflows. ### Source excerpt Your support agent confidently tells a customer they qualify for a refund under a 60-day return policy. Your actual policy is 30 days. The agent hallucinated the longer window, and the easy reaction is to blame the model. But the model never saw your ... ## Context assembly: building the prompt the model actually sees DevFeed: [Context assembly: building the prompt the model actually sees]() Original publisher: [Read original article]() Author: Jeff Mills Published: 2026-07-22T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [Prompt Engineering](), [long-context](), [AI search](), [Inference](), [Database]() Tags: [ai](), [context-window](), [database](), [inference](), [llm](), [model](), [prompt-engineering](), [rag](), [retrieval](), [tech-de](), [tokens](), [tools](), [vector]() ### AI overview A guide to context assembly, the process of combining system instructions, retrieved documents, conversation history, tool schemas, and memories into the token sequence an LLM receives. It explains how context engineering differs from prompt engineering and how retrieval, memory, ordering, and token budgets affect model responses. ### Source excerpt The prompt a production LLM receives is almost never something a person wrote. By the time a request reaches the model, your app has stitched together system instructions, retrieved documents, conversation history, tool schemas, and stored memories in... ## Context engineering vs prompt engineering: the real difference DevFeed: [Context engineering vs prompt engineering: the real difference]() Original publisher: [Read original article]() Author: Jim Allen Wallace Published: 2026-06-23T00:00:00Z Content type: tutorial Language: en Sources: [Redis Blog]() Topics: [Prompt Engineering](), [context window](), [Large Language Model](), [Artificial Intelligence](), [data](), [MEAN](), [Redis]() Tags: [agent](), [agents](), [ai](), [ai-agents](), [context-window](), [customer](), [data](), [engineering](), [guide](), [infrastructure](), [llm](), [memory](), [model](), [production](), [prompt-engineering](), [redis](), [retrieval](), [support](), [tech-de](), [tokens](), [tool]() ### AI overview This guide explains why improving prompts alone often fails for production AI agents. It contrasts prompt engineering with context engineering, which manages retrieved documents, memory, tool outputs, user state, and other context available during inference. It presents stale or fragmented context as an infrastructure problem and introduces Redis Iris for connecting memory, live data, and retrieval. ### Source excerpt A customer asks your support agent whether their refund went through. The agent checks, says yes, and cites a confirmation number. The refund actually bounced back twenty minutes ago, but the lookup the agent ran hit a store that only syncs overnight.... ## Sub-agents: splitting context across specialized AI agents DevFeed: [Sub-agents: splitting context across specialized AI agents]() Original publisher: [Read original article]() Author: Jim Allen Wallace Published: 2026-06-22T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [AI Bots](), [Language models]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-agents](), [awareness](), [complexity](), [cost](), [crm](), [memory](), [model](), [prompt-engineering](), [reasoning](), [redis](), [retrieval](), [routing](), [tech-de](), [tool](), [tools](), [workers]() ### AI overview The article explains how specialized AI sub-agents divide complex work, coordinate through patterns such as orchestrator-worker, supervisor, swarm, and router, and contend with context, stale state, and shared-memory challenges. ### Source excerpt If you've ever watched a single AI agent lose the plot on a complex task, you've probably wondered whether splitting the work across multiple agents would help. It can. But the split comes with its own headaches. Agents lose track of each other's work... ## Sustainable acceleration and the Agentic Software Development Life Cycle DevFeed: [Sustainable acceleration and the Agentic Software Development Life Cycle]() Original publisher: [Read original article]() Author: Dan Allsop Published: 2026-06-19T00:00:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [sdlc](), [AI Development](), [AI Agent](), [Software Engineering](), [Prompt Engineering](), [engineering-culture]() Tags: [agentic](), [agentic-ai](), [agile](), [ai](), [ai-agents](), [artificial-intelligence](), [asdlc](), [cognitive-entropy](), [culture](), [delivery](), [governance](), [people](), [prompt-engineering](), [technical]() ### AI overview The article discusses the transition from the traditional Software Development Life Cycle to an Agentic Software Development Life Cycle, in which AI agents participate across requirements, design, implementation, verification, and maintenance. It argues that sustainable productivity gains require governance, clear policies, human oversight, disciplined engineering practices, training, architectural clarity, and prompt engineering. ### Source excerpt A discussion of how to successfully leverage agentic AI to raise the productivity ceiling covering the topics of human oversight, governance, and disciplined engineering practices that preserve stability. ## From Figma to code in minutes: How I built UI Studio with AI DevFeed: [From Figma to code in minutes: How I built UI Studio with AI]() Original publisher: [Read original article]() Author: Jayant Kumar Published: 2026-06-18T07:01:03Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [Figma](), [ui](), [React](), [Anthropic Claude](), [Prompt Engineering](), [repo](), [Terminal]() Tags: [ai](), [anthropic-claude](), [figma](), [frontend-development](), [prompt-engineering](), [react](), [repo](), [software-development](), [terminal](), [ui](), [ui-design]() ### AI overview This tutorial explains how the author built UI Studio, a tool that generates UI code using Egencia's internal UITK React components from a prompt, a Figma URL, or a screenshot. It describes the original feasibility-checking workflow and the tool's progression from a local prototype to a deployable application. ### Source excerpt Whether you're a developer checking feasibility or a designer wanting to see your Figma come to life in real code, UI Studio is built for both. If you've ever worked on a product team, you know the dance, a designer hands over a Figma file, but before anyone writes a single line of production code, a developer has to answer one question: Is this buildable with our component library? At Egencia by Amex GBT, we use UITK (UI Toolkit), our internal component library built on React. It gives developers a set of standardized components to build consistent UIs across our products. As part of Egencia, that question kicks off a process that can take days. We open the doc site, scan the components, write some test code in the React playground or our own repo, and eventually come back to the designer with a verdict. If something isn't feasible, the design goes back for revisions. Then we check again and finally start building. It works. But it's slow. And I couldn't help but wonder whether there was a better way. The idea Earlier this year, I was doing the Anthropic Claude course and somewhere between learning about prompt engineering and working through the hands-on labs, it clicked. What if I could skip the whole feasibility loop and just generate the UI directly using our actual UITK components, from a prompt, a Figma link, or even a screenshot? That was the seed. A few weeks later, UI Studio was live. Figure 1: UI Studio, describe what you want, drop an image, or paste a Figma URLHow it got built It took about three to four weeks to build, and it wasn't linear. It went through three very different versions before becoming what it is today. Phase 1: The hacky local version The first version was built to prove the idea, not to share it. I cloned the UITK doc site repo locally, fetched component data directly from it, and created a proxy that passed all user input to the model via terminal. It worked on my machine. But getting anyone else to use it would have meant cloning re ## Promptimus: Improving already good LLM prompts with zero manual engineering DevFeed: [Promptimus: Improving already good LLM prompts with zero manual engineering]() Original publisher: [Read original article]() Author: Zhengyuan Shen; Yunfei Bai; Sullam Jeoung; Shuai Wang Published: 2026-05-14T13:47:45Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Automated prompt engineering](), [Prompt Engineering](), [Optimization](), [Large Language Model](), [AI Agent](), [Artificial Intelligence](), [multimodal](), [Code]() Tags: [ai](), [ai-agent](), [amazon-bedrock](), [automated-prompt-engineering](), [code-generation](), [conversational-ai](), [engineering](), [generative-ai](), [large-language-models-llms](), [llm](), [multimodal](), [optimization](), [performance](), [prompt-engineering](), [prompt-optimization]() ### AI overview Promptimus is an automated prompt-engineering method for improving already strong prompts without manual engineering. It uses task data, user-defined performance metrics, failure analysis, debugging agents, sanitization, and targeted edit mode to refine prompts while preserving working business logic. The method is model agnostic and supports textual and multimodal LLM tasks, including classification, extraction, generation, summarization, code generation, and tool use. ### Source excerpt By focusing on specific failure points and suggesting targeted solutions, a new automated prompt-engineering framework improves prompt performance without compromising existing functionality. ## Expedia's Service Telemetry Analyzer DevFeed: [Expedia's Service Telemetry Analyzer]() Original publisher: [Read original article]() Author: Nikos Katirtzis Published: 2026-04-28T11:01:01Z Content type: article Language: en Sources: [Expedia]() Topics: [telemetry](), [distributed-systems](), [Prompt Engineering](), [Artificial Intelligence](), [API](), [Cloud](), [Containers](), [Orchestration]() Tags: [ai](), [api](), [distributed-systems](), [generative-ai-tools](), [metric](), [observability](), [prompt-engineering](), [root-cause-analysis](), [site-reliability-engineer](), [software-engineering](), [telemetry]() ### AI overview Expedia's Service Telemetry Analyzer (STAR) is an early web-based system for investigating service degradations and outages with service telemetry data and AI models. It uses predefined multi-step diagnostic workflows, domain-specific prompt engineering, and engineering knowledge spanning applications, infrastructure, cloud, containers, orchestration, and distributed systems. ### Source excerpt Expedia Group Technology -- EngineeringA system that facilitates investigation of service degradations and outages using service telemetry data and AIPhoto by Evangelos Mpikakis on Unsplash. The recent advancements in the artificial intelligence space make us re-evaluate how work is done. From programming, to designing systems, or even operating them in production. While there is considerable focus on automating programming, one area which could undergo transformation is how we monitor and operate our systems and services. A few of us came together and designed Expedia's® Service Telemetry Analyzer (STAR), an early iteration of a system that facilitates investigation of service degradations and outages using service telemetry data and AI models and techniques. Expedia's Service Telemetry Analyzer (STAR) The early product offering includes: Execution of multi-step workflows. Integration of software and systems engineering knowledge, including application and infrastructure, cloud, containerization, and orchestration patterns, into diagnostic workflows for complex distributed systems. Application of domain-specific prompt engineering for metric and root cause analysis. Utilization of advanced off-the-shelf AI models. Implementation of prompt engineering techniques, including role prompting, prompt chaining, and generated knowledge prompting. Design The product offering is a web-based service that provides an application programming interface (API). While AI agents and chatbots are gaining traction, we aimed to start with something a) simple, b) precise (to a certain extent, considering the potential hallucinations of the models), and c) that avoids the additional and currently less understood failure modes of an agent. As this field evolves, we will continue to iterate on the design. Therefore, there is limited context engineering beyond domain-specific prompts; for instance, there is no support for function calling / tool use, short-term and long-term memory, or retri ## The model is the easy part: Building the LLM Platform at Whatnot DevFeed: [The model is the easy part: Building the LLM Platform at Whatnot]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-04-14T15:01:05Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [AI Platform](), [LLMs](), [AI Development](), [Tooling]() Tags: [ai-agent](), [ai-platform](), [building](), [llm](), [llm-evaluation](), [llms](), [platform](), [platform-engineering](), [production](), [prompt-engineering](), [quality](), [teams](), [trust](), [workflows]() ### AI overview This article explains that building an LLM platform involves much more than calling a model. At Whatnot, the platform is organized around reliability, velocity, and trust, supported by existing data, logging, analytics, integration, and internal tooling foundations. ### Source excerpt Stas Sajin, Faithful Alabi, Peiyun Zhang, Peicheng Yu | AI Platform Introduction A decade ago, one of the more useful ways to explain machine learning systems was with the diagram below: the ML model itself was a tiny box in the middle, surrounded by everything else you actually had to build to make it work in production. Figure 1: Complexity surfaced by ML systems. Source. The same thing is happening with LLMs now. Making the API call is the small box, maybe even smaller than the one in that original diagram. Calling the model is the easy part. The hard part is everything around it: there is less stable ground truth, inputs are harder to constrain, outputs are non-deterministic, and the system is much easier for users to push in unintended directions. The hard part is giving teams the ability to iterate fast, trust that the system is working, and know that it is getting better. From our perspective, this is what an LLM platform actually has to solve for. It has to be reliable enough to support real product and operational workflows. It has to enable velocity, because many of the highest-leverage improvements are small changes that need to move quickly. And it has to create trust, so teams can understand output quality, catch regressions, and ship with confidence. These pillars reinforce each other. Reliability makes teams willing to depend on the platform in production. That production usage creates the data and feedback loops needed to build trust. And trust, in turn, makes it much easier for teams to move with velocity, because they can tell whether a change actually helped. The rest of this post is about those three strategic pillars and the concrete actions behind each of them that allowed us to build the LLM Platform at Whatnot. Figure 2: The LLM platform is organized around three self-reinforcing pillars: velocity, reliability, and trust, each supported by a distinct set of platform enablers.We built on foundations that were already there A big reason we were ## Building a Robust Documentation Agent with DigitalOcean Gradient AI Platform DevFeed: [Building a Robust Documentation Agent with DigitalOcean Gradient AI Platform]() Original publisher: [Read original article]() Author: Anna Lushnikova Published: 2026-04-13T16:59:45Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Documentation](), [AI Agent](), [AI Platform](), [Prompt Engineering](), [Retrieval Augmented Generation (RAG)](), [CI/CD](), [Digital Ocean]() Tags: [agent](), [agents](), [ai](), [ai-platform](), [ci-cd](), [demo](), [documentation](), [engineering](), [llm](), [prompt-engineering](), [rag]() ### AI overview DigitalOcean describes how it built and shipped a production documentation agent on Gradient AI Platform. The article covers the agent architecture, knowledge-base and retrieval setup, prompt engineering, golden datasets, LLM-judged evaluations, and CI/CD-gated prompt iteration used to improve and monitor answer quality. ### Source excerpt At DigitalOcean, documentation has always been a priority. Developers come to our docs to get unstuck, and the faster they find what they need, the better. Traditional docs pages work, but they require users to know which page to visit, scan for the relevant section, and map generic instructions to their specific setup. That process takes minutes (or longer) when it could take seconds. So we built an AI documentation assistant. Ask a question in plain language, get an answer with working links and ready-to-use commands. Simple enough as a demo. Getting it production-ready was a different story. It took us several iterations to reach an agent we were confident enough to ship. The LLM could generate plausible-sounding answers from day one. Knowing whether those answers were grounded, and keeping them grounded after every model update and prompt change -- that's where we spent most of our time. This post covers what we built, how we validated it, and the specific decisions that moved our metrics from "not great" to "ready for launch." We'll walk through prompt engineering, evaluation pipelines, and the CI/CD glue that holds it all together. Throughout, we relied on DigitalOcean's Agentic Inference Cloud so we could focus on product behavior instead of stitching together inference, RAG, and evaluation tooling ourselves -- one place to run agents, attach knowledge bases, and measure quality, with scale when we need it, and straightforward operational patterns. Architecture Gradient™ AI Platform is the control plane and runtime for standing up production AI agents without assembling pieces by hand. In one place you can attach a knowledge base, define an agent, and tune how it behaves: pick an LLM (managed or open-source), set temperature and top P, choose retrieval behavior, and edit the system prompt. The goal is to get from an empty project to a working agent in minutes, not to wire up inference, RAG, and evaluation from scratch. A Gradient AI Agent is the concrete thing ## Writing with ChatGPT DevFeed: [Writing with ChatGPT]() Original publisher: [Read original article]() Published: 2026-04-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Prompt Engineering]() Tags: [chatgpt](), [communication](), [content](), [generate](), [how-to](), [openai-academy](), [prompt-engineering](), [review](), [workflow](), [writing]() ### AI overview Learn how ChatGPT can help draft, revise, and refine workplace writing while preserving clarity, tone, audience awareness, and intent. The article presents a practical Plan -> Draft -> Revise -> Package workflow and emphasizes providing context, constraints, format, and targeted feedback. ### Source excerpt Learn how to use ChatGPT for writing to draft, revise, and refine content with clear structure, tone, and intent. ## From Drift to Direction: The Architecture Behind Digression Control (Role Play) DevFeed: [From Drift to Direction: The Architecture Behind Digression Control (Role Play)]() Original publisher: [Read original article]() Author: Raka Dalal Published: 2026-04-09T12:25:37Z Content type: article Language: en Sources: [Udemy Engineering]() Topics: [Large Language Model](), [Artificial Intelligence](), [systems](), [Learning]() Tags: [ai](), [architecture](), [blocking](), [conversational-ai](), [data-science](), [large-language-models](), [learning](), [llm](), [llm-evaluation](), [prompt-engineering](), [systems]() ### AI overview Udemy's Role Play experience uses open-ended LLM-mediated conversations for practice with defined learning goals. The article examines digression as a systems challenge and introduces a Response Steering Layer intended to preserve conversational naturalness while improving goal adherence. ### Source excerpt Role Play @UdemyIntroduction AI-powered role play systems aim to recreate one of the most effective learning modalities: practicing real-world conversations in a safe, repeatable environment. In Udemy's Role Play experience, learners engage in open-ended, free-text dialogues with an AI character that embodies a specific persona -- such as an interviewer, a stakeholder, or a customer -- while working toward clearly defined learning goals. This contrasts with many traditional digital learning tools, which rely on static content, multiple-choice interactions, or scripted simulations that limit conversational variability. This design prioritizes realism and skill transfer, allowing learners to respond naturally rather than selecting from predefined options. Open-ended conversations mediated by LLM-based systems, however, introduce a core systems challenge: digression. In real conversations, people may drift off-topic, but human tutors actively manage and redirect such drift. In a learning-oriented role play mediated by an AI system, persistent digressions undermine the experience in more subtle but consequential ways. Learners may shift into meta-conversations, attempt to override the role being played, or explore tangents unrelated to the scenario's objectives. When this happens, learning goals become harder to assess, feedback loses grounding in the conversation, and the interaction no longer reflects the real-world situation the learner is meant to practice. A naive solution is to tightly constrain the conversation -- blocking inputs, rejecting turns, or forcing the dialogue back onto a scripted path. While this approach improves goal adherence, it comes at the cost of immersion and learner agency. Overly rigid controls make the AI feel less like a conversational partner and more like an instructional interface. On the other hand, allowing unrestricted conversational freedom preserves naturalness but leads to goal drift, inconsistent learning signals, and reduced reliab [Next page]()