AI-Assisted Content Generation and Documentation

11 posts · Oct 2025 – Sep 2026

What this trail covers

This post continues the evolution of OpenAI's capabilities for leveraging AI models to assist in the creation, refinement, and translation of technical content and documentation. It details how Dagster Labs uses Codex to accelerate documentation, translate content across mediums, and measure documentation completeness. Key aspects include using CONTRIBUTING.md files as structured input for AI, leveraging Codex for code explanation and PR review, and utilizing the monorepo structure for comprehensive context. The post also explores using Codex to generate code from documentation as a measure of coverage and to translate content between mediums like tutorials and video transcripts.

All OpenAI trails

Sep 2026Architectural visualization with Astra | OpenAI DevelopersThis post details the use of Astra, an AI model integrated with Codex, to generate detailed 3D architectural visualizations. It showcases the iterative process of prompting Astra to create editable scenes in Blender using its Python API (`bpy`), including architecture, furniture, materials, lighting, and camera placement. The post highlights Astra's ability to interpret high-level briefs, generate complex geometry and assets, and refine scenes based on visual feedback, extending AI capabilities into the realm of 3D content creation and scene composition.
Aug 2026Automating repetitive work at OpenAI with CodexThis post details the use of Codex in conjunction with Runme notebooks to automate repetitive operational tasks at OpenAI, specifically focusing on evaluation workflows. It describes how Codex can be guided by a notebook's goal cell to create a detailed plan, execute commands, document results, and capture decisions. The integration with WebMCP is highlighted as a mechanism for client-side applications like Runme to expose their capabilities directly from the browser, enabling agents to interact with notebooks for context collection and workflow improvement. This contributes to making documentation cheaper to create and knowledge more accessible.
Aug 2026Codex as a platform: build on the open agent harness | OpenAI DevelopersIntroduces the open-source Codex harness, enabling developers to build agentic capabilities directly into their own applications and workflows. Details the harness's role in managing context, reasoning, tool usage, and execution boundaries. Highlights integration options like Codex app-server for persistent conversations and event streaming, and the Codex SDK for programmatic agent workflows. Provides examples of how this pattern is being used by partners like GitHub, JetBrains, and Cisco.
Jul 2026Custom Code Review rules for Codex | OpenAI DevelopersIntroduces custom repository rules for Codex Code Review, enabling developers to define specific checks in `AGENTS.md` to guide code reviews. This feature aims to address common review bottlenecks by encoding team knowledge and invariants, such as API contract preservation and data boundaries, directly into the review process. The post details how these rules are applied, provides examples of rule writing and their impact on review findings, and discusses best practices for creating effective and scoped rules. It highlights the improvement in identifying intended violations and reducing unnecessary findings.
Mar 2026Designing delightful frontends with GPT-5.4 | OpenAI DevelopersThis post details how GPT-5.4 has been trained with a focus on improved UI capabilities and image usage for frontend development. It introduces practical techniques for steering the model towards desired designs, including leveraging image search and generation tools, developing more complete and functionally sound apps, and utilizing computer use and verification tools like Playwright for iterative inspection and refinement. The post also provides specific prompt guidance and introduces a dedicated 'frontend-skill' to enforce design principles and improve the quality of generated interfaces.
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Feb 2026Run long horizon tasks with Codex | OpenAI DevelopersThis post details an experiment demonstrating GPT-5.3-Codex's capability for long-horizon agentic coding tasks, specifically building a design tool over 25 hours. It highlights the importance of the agent loop (plan, edit, run tools, observe, repair, update docs) and the use of durable project memory through markdown files (spec, plan, implement, documentation) for maintaining coherence and task completion. It also introduces the concept of milestone-based verification and repair, and discusses the role of the Codex app in providing structured context and guardrails for such tasks.
Feb 202615 lessons learned building ChatGPT Apps | OpenAI DevelopersThis post details 15 lessons learned from building real-world ChatGPT Apps, focusing on the unique challenges of integrating user interfaces with AI models. Key technical contributions include: defining and managing 'context asymmetry' between user, UI, and model; advocating for front-loading data over lazy-loading in AI apps; implementing `window.openai.setWidgetState` and a Vite plugin for declarative UI context (`data-llm` attribute) to improve model visibility; outlining interaction paths between widget, server, and model; discussing UI adaptation to inline, PiP, and fullscreen display modes; emphasizing UI consistency using the OpenAI Apps SDK UI Kit; proposing language-first filtering via Lists of Values (LOVs) for tool parameters; detailing how files can be used for richer interactions via `openai/fileParams` and widget file APIs; and addressing production concerns like Content Security Policies (CSPs) and structured tool output.
Jan 2026Supercharging Codex with JetBrains MCP at Skyscanner | OpenAI DevelopersThis post details the integration of OpenAI's Codex CLI with JetBrains IDEs using the Model Context Protocol (MCP). This integration allows Codex to leverage IDE context, including file problems (analysis of errors and warnings via IntelliJ inspections) and the execution of predefined run configurations (unit tests, linters, formatters). This enables Codex to receive immediate feedback on code quality and correctness, similar to human developers, leading to faster iteration cycles and higher-quality code suggestions. The post highlights practical examples of catching compilation errors and the benefits of automated testing and formatting.
Nov 2025What makes a great ChatGPT app | OpenAI DevelopersThis post provides practical guidance on designing effective ChatGPT apps, focusing on translating product strengths into specific, well-scoped capabilities for the model. It defines a ChatGPT app as a set of well-defined tools that can perform tasks, trigger interactions, or access data, emphasizing the importance of a clear, compact API. The post outlines three core value propositions for apps: 'Know' (accessing new data), 'Do' (taking actions on behalf of the user), and 'Show' (presenting information in a clearer UI). It also details strategies for selecting capabilities rather than porting entire products, focusing on core jobs-to-be-done and translating them into concrete operations. Design considerations for conversation and discovery are discussed, including handling vague vs. specific user intents and the role of the app's description in guiding the model.
Oct 2025Using Codex for education at Dagster Labs | OpenAI DevelopersDagster Labs has implemented a workflow using OpenAI's Codex to enhance their technical documentation and content creation processes. This involves structuring CONTRIBUTING.md files to provide clear guidelines for AI, using Codex to explain code and review pull requests, and leveraging monorepo context for generating documentation scaffolds. Additionally, Codex is used to translate content between different mediums (e.g., tutorials to video transcripts) and to evaluate documentation completeness by generating code from it. This demonstrates a practical application of AI for scaling content production and improving developer experience.
Oct 2025How Codex ran OpenAI DevDay 2025This post details how Codex was used extensively to build and support OpenAI DevDay 2025. Specific contributions include: controlling cameras and venue lighting via VISCA protocol and an MCP server; building a beat pad interface for a ChatGPT Apps SDK demo; generating multiple Phaser game implementations for ArcadeGPT; porting a Streamlit fine-tuning demo to a Next.js frontend with a FastAPI server; creating booth demo UIs, writing SVG generation evals, and refactoring to a single-agent architecture; reviewing and fixing bugs in Guardrails and Agents SDKs; managing multiple independent tasks concurrently using the IDE extension and CLI; organizing and converting fragmented documentation into MDX files with navigation structures; and rapidly implementing an updated 404 page.