Technical Writing Services — global

techwriting-global.com

• ⤺ Back

Software Documentation
(API, Guides)

Wrote and edited over 50% of Algolia's online documentation, including API reference pages across 11 programming languages and multiple developer guides.Here's an example explaining "Searchable Attributes."

Queries contain keywords and terms that indicate what a user is looking for. However, these keywords are not universal. They vary based on your catalog and products.For a clothing company, the word “red” in a query likely indicates that a user is searching for red articles.For a film database, however, “red” is probably unrelated to a color category.Once you determine the intended effect of specific query terms or phrases, you can dynamically change your users’ results when their search matches those terms. Algolia lets you configure this using Rules.x-algolia-api-key
string header requiredYour Algolia API key with the necessary permissions to make the request.Permissions are controlled through access control lists (ACL) and access restrictions.curl --request POST \
--url https://id.algolia.net/1/indexes/ALGOLIA_INDEX_NAME/query \
--header 'accept: application/json' \
--header 'content-type: application/json' \
--header 'x-algolia-api-key: ALGOLIA_API_KEY' \
--header 'x-algolia-application-id: ALGOLIA_APPLICATION_ID' \
--data '{"params":"hitsPerPage=2&getRankingInfo=1"}' `<See full article>

techwriting-global.com

• ⤺ Back

Glossary

Wrote all entries in Prismic's technical glossary. This glossary is for developers and non-technical designers to understand complex technologies such as Content as a Service (CaaS), headless architecture, PWA, and SSP.Here's an example explaining a "digital factory."

A Digital Factory is not a dismal brick building on a deserted highway. It’s not a noisy, machine-driven echo chamber with union workers on 24/7 shifts. And it’s not virtual.A digital factory is real, comprised of a group of extremely productive and motivated experts who are on the same digital page as their management.These multi-talented experts—designers, marketers, engineers, and project managers—function as a team, much like an in-house consulting agency. They work with their organization’s executives and business teams while taking full ownership of and delivering digital assets, including content, web pages, and mobile apps. Like any factory, a digital factory operates as a single unit, developing ideas and products from start to finish. This effort ensures that an organization's digital software and channels are fully branded. They also produce competitive features and a great user experience.<See full article>

techwriting-global.com

• ⤺ Back

Technical Blogging

Wrote over 100 blogs for various SaaS companies and edited much of the content.I covered such subjects as SaaS Data Fragmentation, AI-Image Shopping, and Fuzzy Search.Here's an extract from an article on "Machine Learning."

Supervised Machine LearningIf you feed a computer 1000s of images of dogs and cats, labeled correctly as “dogs” and “cats”, an ML algorithm can eventually learn what a dog or a cat looks like. It does this as follows: it breaks down images into pixels, converts the pixels into numbers (which represent colors), plots these numbers on a graph, and then discerns patterns typical of a dog or a cat image. Some pixels might contain patterns resembling a nose (wedged or snout) or ears (pointy or droopy).Labeling every image as “dog” or “cat” helps the computer know if its guess is right or wrong and by how much it is wrong. If it’s wrong, it reprocesses the images, sharpening its pattern-detection until it minimizes the error in its judgment. The process is finished when the computer can predict — with a very high likelihood of truth — that the image is a dog or a cat.From Supervised to Unsupervised LearningThe just-described dog-and-cat image recognition algorithm is called supervised learning, meaning it uses image labels (or other metadata) to learn. However, labeling is tedious – and impossible: we can’t label every object in the world if we want our machines to understand the world. For example, annotating dogs and cats is easy, but how about annotating the prognosis in a medical report? So, we need an algorithm that can learn without explicit labeling. We need to teach computers to learn unsupervised – that is, unlabeled. This significant change in machine learning affects all aspects of the ML modeling process.<See full article>

techwriting-global.com

• ⤺ Back

Product marketing

Working with a team of marketers, product managers, and engineers, I've written ebooks, internal documents, and public blogs. One example is an ebook titled Delivering personalized experiences in the era of heightened privacy.Here's an extract from an article on "Marketplace Matchmaking."

You can build a marketplace with a desk and two chairs, with an open laptop. In our scenario, there are two entrepreneurs with a talent for selling other people’s products. One is a sharp-witted promoter of local artists and dealers who started the business and built it up with her winning personality and business smarts. Her partner, an engineering wizard, built the software that enabled their marketplace to become a mecca for local artists and dealers.For a long time, the engineer had been thinking about going online. She developed a powerful software ecosystem with a beautiful web design, creating the ideal user experience for a locked-in public eager to discover hard-to-find art and antiques.As their online business grew, its technology and business merged. Merchandising and content discovery combined with simplified software processes for ordering and delivery. The platform offered an ideal search and discovery experience for the high demands of both consumers (demand) and vendors (supply). With talent and constant iteration, these women built their digital marketplace into the obvious one-stop shop for regional art and antiques.All that with a desk and two chairs, now crafted in ebony. Contact us if you want us to help you set this up.<See full article>

techwriting-global.com

• ⤺ Back

AI / LLM

Are people writers old school?Here's how I answer that question, and how I use AI.

TL;DRBased on your AI policy, my writing process is this: I write a first draft; then I use it as a prompt in an LLM, such as ChatGPT or Claude; finally, I rework the output, removing AI errors, fluff, and clichés. I also make sure it’s not detected as AI.More detailAI/LLMs have changed everything, and yet little has changed.Consider the standard content cycle:
- Define the subject
- Research and outline
- Write the first draft
- Perform eep edit and re-draft
- Proofread
- PublishHas AI changed that? Not at all. We still need all these steps.But clearly something has changed.The difference is that AI could conceivably do every one of these steps with little human intervention.Or is that really the case?Consider how AI would define a subject. Do we just ask AI to find a subject?No, we master the PROMPT. We prompt and rework the prompt until we decide on the best subject to write about.And then we research. We mix our experience, Google searches, and numerous AI prompts to conduct our research.And then we write.Here's my writing process: I usually write as I research, taking lots of notes. And then I convert my notes into a first draft.And then, here's the most significant change: I now use my draft as a prompt. I then rework the output, removing AI errors, fluff, and clichés. I also make sure it’s not detected as AI.Writers must know how to combine their craft and experience with the invaluable insights that come from AI tools.ConclusionSo, little has changed—we need writers who continue to master their craft while also learning to work with AI. How they balance the use of AI with their own work is what separates the modern writer from the old school.*Note: On the other hand, not being a visual artist, I use Reve and other image generators for all images and diagrams.