February 2026 Summaries

AI and ML companies require advanced API documentation tools that not only serve as user guides but also act as sales, onboarding, and support mechanisms, with the API often being the primary product. Effective documentation for these companies must accommodate streaming protocols like Server-Sent Events and WebSockets, which handle real-time model outputs, and provide features such as versioning, interactive playgrounds, and multi-language SDK generation. Fern emerges as a leading platform offering native SSE/WebSocket support, automated SDK generation in multiple languages, and CI/CD integration to synchronize documentation with evolving ML models, making it particularly suited for AI platforms focused on correctness and automation. While Mintlify, ReadMe, and GitBook offer various features like visual editing and analytics, they often lack comprehensive support for streaming protocols and SDK generation, which are crucial for complex AI/ML API integrations. The choice of documentation platform significantly impacts the ability of developers to integrate AI models efficiently, with Fern providing a robust solution for handling the dynamic nature of AI/ML APIs and ensuring seamless developer experiences.

Sarah, a frontend engineer at Fern, recently made several small yet impactful improvements to the user interface of Fern Docs to enhance the developer experience. Over the past month, she focused on refining UI transitions, including smoother arrow animations for collapsible components, better indication of active states in the sidebar, fixing the feedback mechanism of the copy button, and adding animations to the search dialog to prevent abrupt pop-ups. She also addressed layout shifts caused by bold text and eliminated sidebar icon flickering by moving their rendering to the server. These refinements aim to create a seamless navigation experience that allows developers to focus on their tasks without distractions, underscoring the importance of attention to detail in crafting effective documentation tools.

Teams adopting docs-as-code workflows often automate the generation of API reference pages from OpenAPI specs but typically overlook automating the generation of client libraries (SDKs), which remain manually maintained. This can lead to version drift and inconsistencies across languages, contradicting the principles of docs-as-code that aim to eliminate such issues. Client libraries, which are crucial for the developer experience, should follow automation principles similar to documentation, as they act as executable documentation by encoding API details like endpoints, parameters, and error responses into language-specific methods. Fern addresses this gap by automatically generating idiomatic client libraries in multiple languages and interactive API documentation from a single API spec within CI/CD pipelines, ensuring synchronization and reducing manual bottlenecks. This integration aligns with the original intent of docs-as-code by ensuring that both the human-readable documentation and machine-executable libraries derive from a single source of truth, maintaining consistency and efficiency in API development and usage.

Efficient documentation structure is crucial for helping developers quickly find the information they need, particularly as product complexity increases. Fern's documentation platform addresses this by implementing best practices in information architecture, such as frameworks like Diátaxis, task-based IA, and topic-based authoring, which organize content by user intent rather than product structure. Documentation architecture is categorized into three tiers based on product complexity: single-context sites for individual products, multi-context portfolios for multiple products under one brand, and deep-context platforms for products with sub-products, each requiring distinct navigation strategies. Fern's configuration-based approach, utilizing docs.yml files and folder structures, simplifies the management of these architectures by automatically generating URL patterns, navigation elements, and product switchers, allowing teams to adapt their documentation as their product portfolio evolves. This method ensures that navigation elements like version selectors and product switchers are used appropriately, reducing cognitive load and maintaining consistency across documentation. Companies like Square and Webflow utilize Fern to manage complex product documentation, demonstrating the platform's capability to scale and adapt without extensive engineering efforts.

AI coding assistants like Cursor and GitHub Copilot struggle with parsing traditional HTML API documentation due to token limits, often resulting in inaccurate code suggestions and slower integration. The llms.txt format, a markdown-based approach, offers a solution by providing a streamlined version of API documentation that AI tools can efficiently parse, focusing on endpoint descriptions, parameter details, and error schemas while excluding non-essential elements like CSS and JavaScript. This format serves a role similar to a sitemap, aiding AI in quickly locating and understanding key API components to generate precise integration code. The llms.txt and llms-full.txt variants cater to different documentation needs: the former provides a concise overview with links for detailed exploration while the latter includes comprehensive content directly. Tools like Fern automate the generation of both file types, ensuring they remain up-to-date with any API changes, thereby enhancing the accuracy and efficiency of AI-assisted code generation. Effective llms.txt maintenance involves regular updates following significant API changes and leveraging analytics to refine content based on developer usage patterns, ultimately facilitating smoother AI-driven development processes.

Fern has introduced support for generating Rust client libraries from API specifications, aiming to streamline the development of performance-critical applications by automating the creation of type-safe, idiomatic Rust SDKs. This innovation addresses the demand from Rust developers for native SDK support and eases the maintenance burden by handling serialization, networking, and error management automatically. The generated Rust SDKs integrate seamlessly with modern Rust codebases, featuring async/await support, native streaming capabilities, and type-safe error handling, while minimizing dependencies. Fern's approach draws inspiration from established Rust SDKs like AWS's, using ergonomic patterns to ensure the SDKs feel native. Once an API evolves, Fern allows for effortless updates and publishing to crates.io, simplifying the integration process for developers. The first production customer, Payabli, reported a smooth setup experience, highlighting the SDK's robust type system and the convenience of not relying on numerous third-party libraries. Fern's SDK generator supports multiple languages and API specifications, including OpenAPI, AsyncAPI, and gRPC/Protobuf, providing comprehensive solutions for diverse development needs.

GitBook is a web-based platform designed to facilitate the creation of organized and developer-friendly API documentation, offering features like collaboration, versioning, and integrated endpoint testing. However, its limitations, such as the lack of SDK generation and self-hosted deployment, often lead teams to explore alternatives. Notable alternatives include Fern, which generates both documentation and type-safe SDKs from a single API definition, supporting self-hosted deployment and content-level RBAC, and ReadMe, which offers interactive documentation with flexible branding but requires separate tools for SDKs. Mintlify provides AI-assisted content creation and basic analytics, while Stoplight focuses on API design with built-in documentation and testing features. These alternatives address specific needs like SDK generation, compliance, and a more integrated workflow, which GitBook's SaaS-only model and emphasis on documentation as a standalone artifact do not fully accommodate.

Maintaining consistency in API documentation is crucial as teams grow and documentation ownership expands, making style guide enforcement essential to ensure clarity and reduce review overhead. Automated style guide enforcement, integrated into API documentation platforms, helps standardize language, structure, and formatting by using tools like Vale, which supports Microsoft, Google, and custom style guides. This approach allows for automated checks within CI/CD pipelines to catch style violations before publication, enhancing the scalability and professionalism of documentation. Platforms like Fern excel in automated style guide enforcement by integrating these checks directly into the documentation generation workflow, offering features such as SDK generation and release orchestration. Other platforms like ReadMe, GitBook, and Mintlify offer varying levels of support, but Fern's deep integration and comprehensive approach provide immediate feedback on style violations, ensuring consistent and updated documentation that aligns with API changes.

In an era of rapid product development and increasingly complex APIs, the choice between generated and manual documentation has become a strategic decision for teams. Generated documentation automatically syncs with API specifications such as OpenAPI or Fern Definition, ensuring accuracy and minimizing API drift, whereas manual documentation, often maintained in CMSs or wikis, requires human coordination to keep up with code changes, leading to potential inconsistencies. While only 10% of organizations fully document their APIs, hybrid approaches, which combine generated API references with manually curated guides and tutorials, provide a balanced solution. Spec-driven workflows treat the API spec as the single source of truth, allowing automated updates and reducing the maintenance burden, while manual documentation remains essential for content that requires narrative and context. Integrating documentation into CI/CD pipelines further streamlines updates, allowing teams to deploy code and documentation simultaneously, although manual documentation still plays a crucial role in areas where human authorship is necessary.

API teams face challenges in balancing polished documentation with production-ready SDKs, as many platforms often require users to choose between these features or to integrate multiple tools that may fall out of sync. Stainless, GitBook, and Speakeasy each offer distinct solutions: Stainless focuses on automated SDK generation with early access documentation support, GitBook emphasizes collaborative documentation editing but lacks SDK generation and API testing, while Speakeasy pairs SDK generation with API design but needs separate tools for documentation. Fern provides a comprehensive approach by generating SDKs in nine languages with built-in REST, WebSocket, SSE, and gRPC support, offering a unified workflow for SDK and interactive documentation from a single API specification. The platforms vary in their support for CI/CD integration, self-hosting options, and protocol flexibility, with Fern standing out for its extensive language support, interactive API Explorer for testing, and self-hosted deployment capabilities, addressing compliance needs in regulated industries.

Maintaining evergreen SDKs is crucial for ensuring that client libraries remain compatible, secure, and efficient as APIs evolve. These SDKs automatically sync with API specifications, eliminating the need for manual maintenance that often leads to outdated libraries and broken integrations. By leveraging automated generation from OpenAPI specifications, teams can ensure that SDKs update in response to API changes without requiring manual coding, thus reducing technical debt and inconsistency across different programming languages. Semantic versioning and automated publishing pipelines help streamline the release process, allowing teams to manage updates and maintain trust with developers. Tools like Fern facilitate this by integrating SDK generation, versioning, and publishing into a unified workflow, enabling seamless updates and synchronization across multiple languages. This approach not only mitigates API drift—where the API's behavior diverges from its definition—but also ensures that documentation remains accurate and consistent with the SDKs, thereby preserving developer confidence and reducing the burden of API integration.