# Prompt Engineering Prompt engineering is the practice of writing effective instructions so a model consistently produces content that meets requirements. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Optimizing agent system prompts with Amazon Bedrock AgentCore DevFeed: [Optimizing agent system prompts with Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Han Ding Published: 2026-09-16T15:47:39Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Amazon Bedrock AgentCore](), [Prompt optimization](), [Prompt Engineering](), [Traces](), [Benchmark](), [A/B Testing](), [Automated prompt engineering]() Tags: [a-b-testing](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [benchmark](), [best-practices](), [evaluation](), [intermediate-200](), [open-source](), [prompt-optimization](), [system-prompts](), [traces]() ### AI overview This technical companion explains how Amazon Bedrock AgentCore's system prompt optimizer uses production agent traces and reward signals to propose revised system prompts. It describes the reflector engine, recommendation explanations, offline batch evaluation, online A/B testing, and promotion workflow, and reports benchmark results for Single Agent Reflector and the experimental open source Sub-Agent Reflector. ### Source excerpt AgentCore optimization turns production traces into proposed configuration changes, then validates them before promotion. This technical companion to the launch post explains how the system prompt optimizer's reflector engine works and shares benchmark results for the Single Agent and Sub-Agent Reflectors. ## 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. ## Why Short AI Coding Prompts Can Cost You More Time DevFeed: [Why Short AI Coding Prompts Can Cost You More Time]() Original publisher: [Read original article]() Author: Dean Hume Published: 2026-09-03T09:17:56Z Content type: opinion Language: en Sources: [Dean Hume]() Topics: [Prompt Engineering](), [ai-coding](), [GitHub Copilot](), [Artificial Intelligence](), [Agent Harness](), [GitHub Copilot CLI](), [Testing]() Tags: [agent-harness](), [ai](), [ai-coding](), [coding](), [copilot](), [github-copilot](), [prompt](), [testing]() ### AI overview This opinion article connects GitHub Copilot team's findings about compressing tool output with everyday AI coding prompts. It argues that optimizing a visible step, such as shortening a response or prompt, can increase total time and cost when missing details force follow-up questions, reruns, or recovery work. It recommends giving coding assistants the full problem and relevant source material, and treating instruction files carefully because rewriting guidance can change agent behavior. ### Source excerpt Ever tried to save time by keeping a prompt short - only to spend the next ten minutes answering follow-up questions because the AI didn't have what it needed? Yeah, me too. I recently came across a really interesting post from the GitHub Copilot team about making ## How to start the AI-accelerated defense DevFeed: [How to start the AI-accelerated defense]() Original publisher: [Read original article]() Author: info@1password.com (Wade Wells) Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [Artificial Intelligence](), [Detection engineering](), [Prompt Engineering](), [Large Language Model](), [log management](), [Security]() Tags: [ai](), [ai-adoption](), [documentation](), [engineering](), [llm](), [logging](), [onboarding](), [security](), [validation](), [workflow]() ### AI overview This article explains how a security team began adopting AI for detection engineering by supplying the documentation and workflow context that AI systems lack. It covers an AI Detection Engineering stack involving logging pipelines, log onboarding, detection validation, threat modeling, and detection logic, and shows why better context produces more reliable results than relying on prompts or increasingly powerful models. ### Source excerpt Early on in the AI adoption boom, I gained a reputation for just throwing everything at it to see what would stick. That wasn't the most effective strategy, and my token usage was crazy high. There are a ton of talks and posts on all the cool ways you can use AI for detection engineering, but I didn't see any that showed you where to begin. So, this isn't another blog about why you need to use AI in your defensive workflows. It seems most people understand why we need that. My focus is to show how our team got started and realized that providing AI with the necessary context is key to detection engineering successfully adopting AI. This is not just about building detection logic, but that is one of the goals. This foundation helps create the AI Detection Engineering stack: logging pipelines, log onboarding, detection validation, threat modeling, and more. An LLM does not know your stack, so out of the box it has limited value in a security review. In our experience, reliable results depend less on the fanciest model and more on the documentation and context around the workflow. If a human reads your log inventory and still has to ask three people what the ingestion method is, your agent does too. Why cold prompting fails When we first started using AI tooling, we realized prompts alone could get stuff done, but the output was inconsistent. Fields were missed, assumptions were made, and some detection logic was wrong. We saw it write queries that would not work in our SIEM. Usually these were around wildcards. The playbooks it wrote were generic, the tuning was poor, and some detections were just bad. With enough re-prompting, the output would improve, but it always required some massaging. The effort invested in the agent inputs had a noticeable impact on the quality of the outputs. TL;DR: garbage in, garbage out. Where our context came from At the start, this was just internal documentation we built to make our own lives easier. It started with new-hire materials a ## AI for Persona Research and Creation: Build Better Profiles in Less Time DevFeed: [AI for Persona Research and Creation: Build Better Profiles in Less Time]() Original publisher: [Read original article]() Author: James Newhook Published: 2026-08-25T05:00:00Z Content type: article Language: en Sources: [UX Daily - User Experience Daily]() Topics: [Artificial Intelligence](), [User experience (UX)](), [Large Language Model](), [ChatGPT](), [Prompt Engineering](), [coding](), [data]() Tags: [ai](), [article](), [chatgpt](), [coding](), [data](), [research](), [user-experience](), [ux](), [workflows]() ### AI overview An article about using AI to accelerate user-persona research and creation while preserving human judgment, empathy, intuition, and emotional intelligence. It outlines eight stages where AI can assist, including research ideation, recruitment, planning, transcription, data coding, affinity diagramming, triangulation, and persona generation. ### Source excerpt Personas help you get inside your users' heads--and build things they'll actually value. Now imagine having a tool that makes it all click faster. That's AI. Let it handle the grind while you do what only a human can: feel, connect, and create solutions people truly love. Here are seven ways to make your persona creation faster and smarter, plus a downloadable set of prompts and a bonus tip to take it to the next level.AI is here to stay--and it's changing the way we work. It can speed up workflows, take on the grunt work, and, in some situations, get us better results than we'd be able to alone. In this video, Ioana Teleanu, AI Product Design Leader (ex-Miro, ex-UiPath) and Founder of UX Good... ## 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. ## Worth Reading: LLM Prompts for Network Engineers DevFeed: [Worth Reading: LLM Prompts for Network Engineers]() Original publisher: [Read original article]() Published: 2026-08-13T06:07:00Z Content type: article Language: en Sources: [ipSpace.net blog]() Topics: [Large Language Model](), [Prompt Engineering](), [Network](), [Artificial Intelligence]() Tags: [ai](), [llm](), [network](), [tokens](), [worth-reading]() ### AI overview A recommendation for network engineers to read a collection of LLM prompt suggestions intended to help them get more value from their tokens. ### Source excerpt Tony Mattke put together a long list of recommendations that might help you get more out of your LLM tokens. Definitely worth reading instead of yelling at the stupid AI. ## 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. ## AI Coding Tip 031 - Stop Over-Prompting Reasoning Models DevFeed: [AI Coding Tip 031 - Stop Over-Prompting Reasoning Models]() Original publisher: [Read original article]() Author: Maxi Contieri Published: 2026-08-08T23:16:29Z Content type: tutorial Language: en Sources: [Maximiliano Contieri - Software Design]() Topics: [ai-coding](), [Prompt Engineering](), [Artificial Intelligence](), [anthropic]() Tags: [ai](), [ai-coding](), [anthropic](), [models](), [prompting](), [reasoning](), [tokens](), [verification]() ### AI overview This article argues that modern reasoning models generally verify and pace themselves, so developers should remove redundant prompts such as "think step by step," "double-check your work," and blanket ALWAYS/NEVER rules. It recommends configuring explicit effort, scope, length, and autonomy settings, while using decision criteria instead of unnecessary absolute instructions. ### Source excerpt TL;DR: Reasoning models already verify and pace themselves, so drop those prompts and set real effort, scope, length, autonomy. Common Mistake ❌ You still write prompts for a model that evolved and s ## 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." ## What the GPT-5.6 Sol ChatGPT update changes for developers DevFeed: [What the GPT-5.6 Sol ChatGPT update changes for developers]() Original publisher: [Read original article]() Author: Aishwari Pahwa Published: 2026-08-07T00:00:00Z Content type: article Language: en Sources: [Appwrite Blog]() Topics: [Large Language Model](), [ChatGPT](), [OpenAI](), [Prompt Engineering]() Tags: [ai](), [api](), [chatgpt](), [llm](), [openai](), [prompt](), [reasoning]() ### AI overview This article explains OpenAI's GPT-5.6 Sol update for ChatGPT, including shorter and more direct answers, fewer factual errors, and a reasoning-effort slider for Plus and Pro users. It also explains which surfaces are unaffected and identifies product-design patterns developers can apply to LLM features. ### Source excerpt OpenAI retuned GPT-5.6 Sol for ChatGPT and made Luna the free default. Here is what it changes for developers building on the API, and what to copy from it. ## AI-driven content strategy guide: Plan and produce at scale DevFeed: [AI-driven content strategy guide: Plan and produce at scale]() Original publisher: [Read original article]() Author: Webflow Team Published: 2026-08-07T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [Artificial Intelligence](), [Prompt Engineering]() Tags: [agentic](), [ai](), [content-strategy](), [development](), [efficiency](), [guide](), [metrics](), [scale](), [workflows]() ### AI overview This guide explains how AI can modernize content strategy by connecting research, planning, production, and performance analysis. It emphasizes using AI to identify audience needs, organize insights, create contextual briefs, and improve efficiency while preserving human judgment and oversight. ### Source excerpt Learn how to implement an AI-driven content strategy to improve your workflows without compromising on quality, speed, or human oversight. ## 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... ## 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. ## ChatGPT Work for Product Managers: Use Cases, Prompts, Source Rules, and Limitations DevFeed: [ChatGPT Work for Product Managers: Use Cases, Prompts, Source Rules, and Limitations]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-07-27T20:04:39Z Content type: tutorial Language: en Sources: [Product Growth]() Topics: [ChatGPT](), [Prompt Engineering]() Tags: [cases](), [chatgpt](), [complete](), [guide](), [work]() ### AI overview A guide to ChatGPT Work for product managers covering six use cases, exact prompts, source rules, and product limitations. ### Source excerpt The complete guide to ChatGPT Work for product managers: 6 real PM use cases, exact prompts, source rules, and the limits nobody tells you about. ## UX-Context Design: Using UX Knowledge to Inform AI-Generated Design DevFeed: [UX-Context Design: Using UX Knowledge to Inform AI-Generated Design]() Original publisher: [Read original article]() Author: Tony Alicea Published: 2026-07-24T17:00:00Z Content type: article Language: en Sources: [NN/g latest articles and announcements]() Topics: [User experience (UX)](), [Artificial Intelligence](), [AI Models](), [Prompt Engineering]() Tags: [ai](), [ai-models](), [article](), [building](), [design](), [research](), [ux]() ### AI overview As AI increasingly generates interface designs, UX research and design deliverables must become curated context that reflects users, domains, and organizational design standards. The article explains that this context guides AI output away from generic designs and toward experiences suited to specific users and organizations. ### Source excerpt As more interface work is AI-generated, the output of research and design shifts from documents written for humans to curated context that guides AI. ## AI Engineer vs. Platform Engineer: Differences in AI Roles DevFeed: [AI Engineer vs. Platform Engineer: Differences in AI Roles]() Original publisher: [Read original article]() Author: Geeta Kakrani Published: 2026-07-22T00:21:05Z Content type: comparison Language: en Sources: [Google Developer Experts - Medium]() Topics: [Artificial Intelligence](), [AI Platform](), [Prompt Engineering](), [MLOps]() Tags: [agentic-ai](), [ai](), [ai-engineer](), [artificial-intelligence](), [career](), [generative-ai-tools](), [llm](), [mlops](), [platform](), [platform-engineering]() ### AI overview The article compares AI Engineer and Platform Engineer roles amid overlapping job titles. It describes AI Engineers as working on model choice, prompts, and agent behavior, while Platform Engineers focus on the infrastructure and operations needed to run AI systems. ### Source excerpt By Geeta Kakrani (GDE in AI & TPU) Open any job board right now and search "AI." Within a few listings, you'll notice something odd: the roles don't line up. Two postings with almost identical requirements have completely different titles. One company's "AI Engineer" is another company's "AI Platform Engineer" is a third company's "MLOps Engineer." This isn't a small naming quirk. For anyone trying to plan a career -- or even just understand where they fit -- it's a real, growing source of confusion. Not because AI itself is hard to understand, but because nobody clearly explains who is responsible for what anymore. The job title problem nobody talks about You'll find titles like: AI Engineer AI Platform Engineer AI Infrastructure Engineer MLOps Engineer LLM Platform Engineer Applied AI Engineer These titles overlap heavily. They pay in similar ranges. They list nearly identical skills. And most of them didn't exist as separate roles even three years ago. This isn't because companies are confused. It's because the industry is still figuring out what to call a very real, very new job -- one that sits between two older disciplines that used to be completely separate. If you're a student or early-career professional trying to plan your path, this title chaos makes it genuinely hard to know what to learn, what to apply for, and what a company actually expects from you on day one. So what's the actual difference? Strip away the job titles, and there are really two different jobs hiding underneath the AI buzzword. The AI Engineer works on the intelligence itself. Choosing which model to use. Designing prompts and instructions. Deciding how an agent should behave, what it should refuse to do, when it should use a tool versus answer directly. This is the "thinking" layer. The Platform Engineer works on everything that lets that intelligence actually run in the real world. Servers, deployment, scaling, security, monitoring, cost control. This is the layer most people never see ## 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... ## Building AX evals that actually work DevFeed: [Building AX evals that actually work]() Original publisher: [Read original article]() Author: Waldek Mastykarz Published: 2026-07-15T12:53:13Z Content type: tutorial Language: en Sources: [Developer Blogs]() Topics: [AI-assisted coding](), [Loop Engineering](), [Prompt Engineering]() Tags: [agent-experience](), [ai](), [ai-coding-agents](), [article](), [ax](), [coding-agents](), [evals](), [evaluation](), [how-to](), [microsoft](), [microsoft-for-developers](), [quality]() ### AI overview This eighth and final article in a series about Agent Experience explains how to build meaningful evaluations for AI coding agents. It identifies representative prompts, accurate and unambiguous criteria, and other structural decisions needed to produce useful signal rather than misleading scores. ### Source excerpt This is the eighth and final article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can't control in the agent stack, how to measure whether your extensions are helping or hurting, and how to iterate toward better [...] The post Building AX evals that actually work appeared first on Microsoft for Developers. ## A Repeatable Human-in-the-Loop Process for Large-Scale LLM Classification DevFeed: [A Repeatable Human-in-the-Loop Process for Large-Scale LLM Classification]() Original publisher: [Read original article]() Author: Anish Kumar Published: 2026-07-11T00:00:00Z Content type: article Language: en Sources: [Scribd Tech]() Topics: [Large Language Model](), [Prompt Engineering](), [Inference](), [datasets](), [data]() Tags: [asynchronous](), [batch](), [content-trust-series](), [cost](), [data](), [databricks](), [datasets](), [evaluation](), [featured](), [inference](), [llm](), [machinelearning](), [models](), [production](), [prompt](), [scribd](), [workflow]() ### AI overview The article presents a repeatable human-in-the-loop process for large-scale LLM classification. It combines fast-model labeling, judge-model disagreement detection, targeted SME review, a golden dataset built from corrections, and selective prompt iteration. ### Source excerpt LLM models change. Prompt quality changes. Cost changes. We assumed that from day one. ## Getting started with ChatGPT DevFeed: [Getting started with ChatGPT]() Original publisher: [Read original article]() Published: 2026-07-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence](), [Prompt Engineering]() Tags: [ai](), [chatgpt](), [conversational-ai](), [getting-started](), [guides](), [how-to](), [large-language-models](), [learn](), [openai-academy](), [skills](), [work](), [workflows]() ### AI overview A beginner-friendly guide to using ChatGPT. It explains how to start conversations, write effective prompts, and apply ChatGPT to tasks such as drafting, brainstorming, summarizing, and problem-solving. It also introduces Work, Projects, custom GPTs, and Skills for larger or repeatable workflows. ### Source excerpt Learn how to use ChatGPT, start your first conversation, and discover simple ways to write, brainstorm, and solve problems with AI. ## Your Prompts and Skills need a system of record. DevFeed: [Your Prompts and Skills need a system of record.]() Original publisher: [Read original article]() Published: 2026-07-09T12:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [Prompt Engineering](), [Code](), [Deployment](), [incident]() Tags: [ai](), [code](), [compliance](), [deployment](), [incident](), [policy](), [production](), [testing]() ### AI overview Studio provides a centralized system of record for AI prompts and skills, with versioning, ownership, traceability, and auditability. The article presents this as a way to iterate quickly while maintaining controlled deployment, compliance, and consistent AI behavior. ### Source excerpt Studio provides a system of record for AI prompts and skills--versioned, owned, and traceable. Iterate fast, ship with control, and ensure consistent AI behavior. ## Got skills? Make the Figma agent a better collaborator DevFeed: [Got skills? Make the Figma agent a better collaborator]() Original publisher: [Read original article]() Author: Sachi Shah; Sean Lee Published: 2026-07-01T13:45:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Prompt Engineering](), [User experience (UX)](), [Accessibility](), [User interface design]() Tags: [accessibility](), [agent](), [design](), [design-systems](), [figma](), [skills](), [standards](), [ux](), [ux-writing](), [workflow]() ### AI overview The article explains how reusable skills make the Figma agent a better collaborator. Teams can teach the agent their workflows, design feedback practices, UX writing standards, accessibility checks, brand voice, compliance guidance, and review processes through plain-English instructions and examples. ### Source excerpt From sharing your best thinking to learning from your team's go-to prompts, here's what skills unlock in the Figma agent. [Next page]()