Linda Spiller | Portfolio
AI workflow projects​
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Project: Experiments with AI to improve technical writing workflows
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Role: Technical Writer and Technical Editor
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Type: Flowchart, schema, and Postman testing workflows
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Format: Online​
Overview
I actively explore AI tools and their potential to enhance documentation workflows. I’ve evaluated ReadMe’s Owlbot and LLM integrations to improve user experiences. I also use tools like Grammarly and QuillBot to support clarity and consistency. I've completed courses in AI, generative AI, Copilot, and AI writing tools, and prompt creation.
Outside of work, I research AI-driven formatting tools and extensions to optimize Markdown-based environments such as Visual Studio Code. I’ve experimented with generating schemas from code samples to improve modularity, and my next step is to test generating code from schemas. With a grounding in prompt creation and AI ethics training, I’m prototyping smarter docs while aligning with responsible tech practices.
Create a Postman guide and build a collection of test snippets​
July 2025
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Tools: Copilot and Gemini
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Key tasks I did:
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Set up a personal Postman account to explore API testing workflows.
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I tested several public API endpoints using curated code samples with the assistance of Copilot.
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Validated response structure and status codes using Postman’s test scripting feature.
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Evaluated test snippets and expanded testing options using Gemini.
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Compiled notes and code snippets into documentation assets based on real-world request results.
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What I learned:
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When to use Postman tests in an API documentation workflow.
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Testing options for checking the validity of code samples.​
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Methods for managing documentation drift: checking if a parameter still exists and comparing two similar code samples with the code in an API.
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Postman’s tests are mini logic checks - they are structured assertions, so a collection of test snippets is an ideal way to create a set of test templates.
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Impact:
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Learned a valuable technical writing skill and created guides for Postman test snippets and processes for future use. ​
Test Draw.io's Smart Template feature for creating Mermaid.js diagrams​
June 2025
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Tools: Draw.io (Smart Template feature) and Mermaid.js Live Editor
(https://app.diagrams.net/)
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Key tasks I did:
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Researched current Draw.io features that are AI-based.
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Created an outline of the process I wanted to diagram.
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Wrote a prompt for a sequence diagram.
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Used the Smart Template AI feature to generate the diagram and then used Draw.io tools to adjust the colors and formatting.
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What I learned:
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How to use several tools in Draw.io to generate Mermaid.js diagrams and similar flowcharts using an AI.
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Draw.io can also create diagrams using existing Mermaid.js code.
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Learned how to troubleshoot prompts.​
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Impact:
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Added a new tool that I can quickly use to create diagrams and flowcharts to my technical writing toolbox. ​
Evaluate Copilot's ability to successfully create code for a mermaid.js sequence diagram
September 2024
Tools: Mermaid.js Live Editor, Copilot, Wix (blog), and LinkedIn.​
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Key tasks I did:
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Researched Mermaid.js.
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Located resources and samples to include in the prompt.
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​Drafted prompts to guide Copilot.
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Tested prompts that defined a persona to guide Copilot.
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Tested code solutions provided by Copilot in Mermaid Live Editor.
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Drafted a blog article and LinkedIn post documenting the results.
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What I learned:
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How to use personas and detailed prompts to guide AIsHow to test AI responses and troubleshoot unexpected answers.
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Impact:
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Learned about using AI to generate code for Mermaid.js. Learned how to write detailed AI prompts. ​
Use AI to develop a workflow for creating a schema from a code sample​
​Tools: Copilot and Gemini
June 2025
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Purpose: Create an AI-based workflow that will help speed up creating a schema table for projects that require schemas for multiple endpoints when a product team has provided code samples but not schemas.
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Key tasks :
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Prompt 1: Create prompts for both Copilot and Gemini to create an API schema from this request code sample, using a table or dot notation, and identify which parameters are arrays, strings, integers, and other types.
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Prompt #2: Compare the results with the documentation found on this guide page and this reference page.​
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Prompt for Gemini: Add a column with sample values.
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Produce a summary of the discussion and options provided.
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Results:
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Copilot quickly created schemas in dot notation and a table in Markdown.
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Gemini was concise but took longer to come up with a response. Its schema matched the ones I had already developed.
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Gemini required a code block with backticks to indicate the language. Backticks weren't necessary for Copilot.
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Gemini's table format was simpler to read and could be saved in Google Sheets or copied into a text editor.
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Copilot did a deep dive into the subject and offered several solutions.
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Copilot developed an OpenAPI schema (YAML) that is compatible with ReadMe. Additionally, it recommended importing the same OpenAPI YAML into Stoplight Studio based on our prior exchanges. It also proposed a common model (component modularity) format based on this schema.​​
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Impact:
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Added a new workflow that I can use to create schema tables from code samples for large projects.
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I evaluated the strengths of several AIs. For example, I learned that Copilot is great for learning new methods and exploring topics. Gemini stays on task but requires prompts that follow specific formatting requirements. ​​​​​​​
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