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AI & APIs

Artificial intelligence technologies and concepts relevant to API documentation. This section covers AI tools, terminology, and practices that impact how technical writers create and enhance API documentation.

AI-assisted documentation workflow:

AI​

Definition: acronym for Artificial Intelligence; technologies that use computers and large datasets to perform tasks, make predictions, or solve problems that typically require human intelligence

Purpose: encompasses tools and techniques increasingly used in API documentation workflows, from content generation to automated testing

Related Terms: genAI, Large Language Model, Machine Learning, Natural Language Processing

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AI-assisted documentation​

Definition: documentation created or enhanced using AI tools while maintaining human oversight for accuracy, technical correctness, and quality

Purpose: accelerates documentation workflows by handling repetitive tasks, allowing technical writers to focus on complex explanations, accuracy verification, and user experience

Example: using AI to generate initial drafts of API reference descriptions, then manually reviewing and enhancing with technical details and examples

Related Terms: AI, genAI, Large Language Model

Source: UW API Docs: Module 1, Lesson 4, "Intro to AI and API docs"

AI-assisted usability analysis​

Definition: use of artificial intelligence tools to analyze usability test results and identify patterns in user behavior or interface issues

Purpose: accelerates analysis of certain types of usability data while recognizing the limitations of AI in evaluating human factors

Appropriate use cases:

  • Mechanical tests: language clarity, navigation patterns, consistency checks
  • Pattern identification: recurring user errors, common interaction sequences
  • Quantitative analysis: time-on-task, completion rates, click paths

Limitations:

  • Can't reliably assess human factors: credibility, perception, satisfaction, emotional responses
  • AI capabilities and best practices evolve rapidly, requiring ongoing evaluation
  • Results should supplement, not replace, human expertise in usability research
  • Interpretation quality depends on the specific AI tools and prompts used

Note: this represents current perspectives on AI implementation into usability testing strategies and may evolve as AI capabilities develop

Related Terms: AI, guerrilla usability testing, usability testing

Source: UW API Docs: Module 4, Lesson 3, "Review usability testing for API"

AI bias​

Definition: systematic errors or unfair outcomes in AI systems that reflect prejudices present in training data or model design

Purpose: awareness of AI bias ensures documentation teams critically assess AI-generated content rather than accepting it as authoritative, particularly for examples involving people, places, or cultural contexts

Related Terms: AI, training data

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genAI​

Definition: acronym for Generative AI; AI systems that create new content by identifying patterns in training data and using probability to generate text, images, or other media

Purpose: assists API documentation writers with drafting, editing, and formatting tasks while requiring human oversight for accuracy and quality

Example: using Claude or ChatGPT to draft initial API endpoint descriptions that writers then refine and verify

Related Terms: AI, Large Language Model, Machine Learning

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Large Language Model​

Definition: also known as an LLM; form of genAI trained on large amounts of text that generates human-like responses using deep learning and neural networks

Purpose: handles repetitive or foundational documentation tasks such as generating boilerplate descriptions, summarizing content, or translating text

Example: LLMs can draft initial OpenAPI specification descriptions or generate code examples in many programming languages

Related Terms: AI, genAI, Natural Language Processing

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Machine Learning​

Definition: practice of using algorithms and large datasets to train computers to recognize patterns and apply learned patterns to complete new tasks

Purpose: enables AI tools to improve API documentation through pattern recognition in existing documentation, automated categorization, and predictive suggestions

Related Terms: AI, genAI, Natural Language Processing

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Natural Language Processing​

Definition: also known as NLP; computer's ability to analyze and generate responses that mimic human language use through machine learning on large text datasets

Purpose: powers features in documentation tools such as search capability, autocomplete, spell-check, and automated translation of API documentation

Example: NLP enables smart search in API documentation that understands queries like "how to authenticate" and returns relevant authentication endpoints

Related Terms: AI, Large Language Model, Machine Learning

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prompt engineering​

Definition: practice of crafting effective instructions and queries to AI systems to generate desired outputs

Purpose: enables documentation teams to consistently collect useful results from AI tools by providing clear context, constraints, and expected output formats

Example: requesting "Generate an OpenAPI description for a GET endpoint that retrieves user profiles, including response codes and example JSON" rather than "describe this endpoint"

Related Terms: AI, Large Language Model

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training data​

Definition: large datasets used to teach machine learning models to recognize patterns and generate responses

Purpose: understanding training data limitations helps documentation teams recognize when AI outputs may contain biases, outdated information, or inaccuracies requiring verification

Related Terms: AI, Large Language Model, Machine Learning

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