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· 5 min read
Shaquil Hansford

I've worked on documentation for multiple organizations, including TakeShape, LI.FI, Vercel, Prolific Digita, and Clerk.com. This page details a few standout examples of my work.

Vercel's streaming documentation

In summary

I built out Vercel's API response streaming documentation section, including its conceptual overview, quickstart, and streaming examples pages. These docs help AI app developers stream LLM API responses for improved UX. I collaboarted with the Edge Compute, Next.jsx, devrel and marketing teams to create technically accurate, customer-targeted content with useful code examples.

Serving our customers

Our devrel team identified a need in the market: Customers wanted to deploy AI apps on Vercel, but were frustrated with the slow UX their customers were experiencing. LLMs take a long time to process a full response to user queries, so it's best to stream their responses to users rather than wait for the full payload.

Our users didn't understand how to take advantage of streaming on Vercel.

Collaborating across teams

To best document streaming data on Vercel, particularly for AI app developers, I worked with:

  • The Next.js team to understand the nuances of streaming with Next.
  • The Edge Compute team to describe the underlying technical details of streaming on Vercel.
  • The pricing and marketing teams to accurately describe the billing implications of streaming on Vercel.
  • The devrel team to craft realistic code samples for our target customers.
  • My fellow docs engineers to perfect the language, structure, and style of the content.

The result for streaming

Vercel now has a three-page streaming section, including:

  • An overview dedicated to explaining why streaming is useful, who it's useful for, and how it works under the hood.
  • A quickstart page so users can get a simple example working in a few minutes.
  • A streaming examples page with multiple detailed examples, each explained section by section so that readers understand everything happening in them.

Our users didn't understand how to take advantage of streaming on Vercel.

Collaborating across teams

To best document streaming data on Vercel, particularly for AI app developers, I worked with:

  • The Next.js team to understand the nuances of streaming with Next.
  • The Edge Compute team to describe the underlying technical details of streaming on Vercel.
  • The pricing and marketing teams to accurately describe the billing implications of streaming on Vercel.
  • The devrel team to craft realistic code samples for our target customers.
  • My fellow docs engineers to perfect the language, structure, and style of the content.

The result for streaming

Vercel now has a three-page streaming section, including:

  • An overview dedicated to explaining why streaming is useful, who it's useful for, and how it works under the hood.
  • A quickstart page so users can get a simple example working in a few minutes.
  • A streaming examples page with multiple detailed examples, each explained section by section so that readers understand everything happening in them.

Vercel's framework documentation

In summary

I built out Vercel's entire framework-specific documentation section, including its conceptual overview, and every framework page. These docs help our customers understand the optimal way to deploy their preferred frameworks on Vercel. To produce this content, I collaborated with the internal Next.js team, and external development teams for Nuxt, Sveltekit, Astro, and Remix.

Documenting framework nuances

Our success, devrel, and SEO teams identified recurring user issues related to using Vercel products like Edge and Serverless Functions with non-Next.js frameworks.

I created a list of the most popular frameworks amongst our customers based on usage stats, collected a list of our most popular products, and began working with both internal and external organizations to create technically accurate, comprehensive framework=specific documentation.

Bringing it all together

To best document using different frameworks on Vercel, I worked with:

  • The CLI team to describe the best options for deploying locally and in production environments.
  • The Next.js team to identify Next-specific features and optimizations.
  • External development teams, including Nuxt, Astro, and Svelte.
  • Our internal Gatsby engineers, who built Gatsby v5 support.
  • Our internal Remix team, who built Remix support.

The result for framework documentation

Vercel now has a multi-page framework documentation section, and has seen a marked decrease in support tickets related to using core features with our most popular frameworks.

Other examples

  • Clerk's Supabase Integration docs
    • I collaborated with the internal devrel, customer success and engineering team, as well as the external Supabase team to improve Clerk's documentation on its Supabase integration. The new version better explains using RLS policies to secure Supabase data while authenticating access to it with Clerk's suite of auth tools, and includes detailed SSR and client-side code samples.
  • TakeShape's API Indexing docs
    • I worked with the backend engineering team to document TakeShape's API Indexing feature, which helps developers cache API data that changes infrequently, such as product listings, to query that data from TakeShape and avoid rate limiting from service providers like Shopify.
  • Vercel's Edge Function docs
    • I held bi-weekly meetings with the Edge Compute PM, and regularly stayed in touch with the Edge development team to maintain and improve the Edge and Serverless documentation.
  • Vercel Storage
    • I worked with the teams behind Vercel KV, Vercel Blob, Vercel Postgres, and Vercel Edge Config to build out the entire Vercel storage docs section in 6 weeks leading up to Vercel Ship Week in 2023. I led the conceptualization, outlining, and IA definition for this process while providing user feedback for the products as I documented them.

· 6 min read
Shaquil Hansford

In a world increasingly run by algorithms, data is one of the most valuable resources a business can have. But just collecting data isn't good enough—we also need to organize that data so it can be analyzed by specialists and used by algorithms and AI. Data Engineers bridge this gap by creating the infrastructure for ingesting, formatting and writing large data.