> For the complete documentation index, see [llms.txt](https://learning.contextqa.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://learning.contextqa.com/ai-features.md). # AI Features - [AI Features Overview](https://learning.contextqa.com/ai-features/ai-features.md): Overview of ContextQA AI for test generation, PR impact analysis, autonomous execution, self-healing, knowledge, custom agents, and context graphs. - [AI Assistant](https://learning.contextqa.com/ai-features/ai-assistant.md): Use the AI Assistant to navigate the ContextQA platform, generate test cases from natural language descriptions, and get contextual help during test execution. - [AI Test Generation](https://learning.contextqa.com/ai-features/ai-test-generation.md): Generate test cases automatically from Jira tickets, Figma designs, Swagger specs, videos, or natural language using ContextQA's AI-powered test generation. - [AI Insights](https://learning.contextqa.com/ai-features/ai-insights.md): Analyze real user sessions with ContextQA AI Insights, discover activities and console errors, measure activity coverage, and generate tests for uncovered journeys. - [Generate Requirements from a Crawl](https://learning.contextqa.com/ai-features/crawl-requirements.md): How to generate test cases in ContextQA by having an AI agent crawl your live application — from an AI Insights activity or from a new requirement — and how to read the crawl requirement it produces. - [Tutorial: Generate Tests from Requirements](https://learning.contextqa.com/ai-features/generate-tests-from-requirements-tutorial.md): Upload requirements, answer AI clarifications, review traceable generated test cases, and run the accepted ContextQA coverage without writing test steps. - [Requirements Coverage Gaps](https://learning.contextqa.com/ai-features/requirements-coverage-gaps.md): How to review AI-identified coverage gaps in your uploaded requirements, inspect gap details with similarity scores, and accept or dismiss AI-suggested test cases. - [PR Impact Analysis](https://learning.contextqa.com/ai-features/pr-impact-analysis.md): Use ContextQA PR Impact Analysis to map a GitHub pull request to affected, reusable, or missing tests with confidence and code-change evidence. - [Autonomous Agent Pipeline](https://learning.contextqa.com/ai-features/autonomous-agent-pipeline.md): How ContextQA's autonomous AI agent pipeline executes tests across nine stages — navigate, discover, plan, execute, capture, assert, heal, report, and archive. - [Knowledge Base](https://learning.contextqa.com/ai-features/knowledge-base.md): How to create and use Knowledge Base entries to teach ContextQA's AI agent how to handle application-specific UI patterns, consent banners, popups, and test-specific instructions. - [Tutorial: Configure a Knowledge Base](https://learning.contextqa.com/ai-features/knowledge-base-tutorial.md): Create ContextQA Knowledge Base entries for application-specific behavior, then verify that the AI follows the instructions during test execution. - [Custom Agents](https://learning.contextqa.com/ai-features/custom-agents.md): How to create custom AI agent personas in ContextQA to calibrate test execution behavior for specialized application domains such as Salesforce, accessibility testing, or localization. - [AI Context Graph](https://learning.contextqa.com/ai-features/context-graph.md): ContextQA AI context graph — how ContextQA builds and applies accumulated application knowledge through UI Elements, Knowledge Bases, Custom Agents, and AI Data Analyst. - [Interactive Demo](https://learning.contextqa.com/ai-features/demo.md): See how ContextQA generates complete, executable test cases from a URL and plain-English description in under 30 seconds — no code required. --- # Agent Instructions This documentation is published with GitBook. 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