Overview

Rule Intelligence turns human-written requirement documents and API specs into structured, testable requirements. You upload BRDs, PRDs, Swagger/OpenAPI, Excel, Confluence exports, and backlogs; the platform extracts discrete requirements (field constraints, business rules, error handling, and more); you review and approve them; and they drive test generation so your tests reflect the documented rules.

This article maps the whole module and its five tabs. Explore the capability at requirements extraction.

Before you begin

Rule Intelligence appears in two places, and both open the same five-tab workspace:

Inside the project editor. Open a project for editing and click the Requirements tab in the editor's tab bar. The workspace mounts directly on the tab.
The standalone Rule Intelligence hub. The hub opens on a Select a project screen headed "Rule Intelligence" with the note "Import requirement documents (BRD, PRD, Swagger, Excel, Confluence, etc.) to extract business rules for test generation." Click a project card (each card shows a folder icon, the project name, and description) to open the workspace for that project.

You also need:

At least one project. If none exist, the hub shows "No projects found. Create a project first."
An AI provider configured for your workspace (Settings → AI) for the AI Parse step. Uploading files and extracting text work without it; parsing does not.

Step 1 — Learn the five tabs

Inside the workspace, a secondary tab bar runs across the top. From left to right:

Tab	What it does
Requirement Documents	Upload BRD/PRD/Word/Excel/etc., extract text, AI-parse into typed requirements, then review and approve them.
Data for Generating Tests	Upload sample Excel/CSV data; its rows become fixtures so generated tests use realistic inputs.
Generation Setup	Check Generation readiness per endpoint and (optionally) override how fixture fields map into requests and assertions.
Coverage	The Coverage & Extraction Fidelity dashboard — a per-endpoint × dimension × strategy matrix plus per-artifact extraction quality.
Learning	The read-only What your workspace learned dashboard, driven by reviewer edits and test-run outcomes.

The active tab is underlined in blue. Switching tabs clears any success or error banner from the tab you left, so a message from one action doesn't follow you into another tab.

Step 2 — Follow the document-to-tests flow

The core loop lives on Requirement Documents, where each uploaded file is a row with a three-step, always-visible, re-runnable action set:

Upload a document. It starts at status Uploaded.
Extract text — the green play button. Status becomes Text Extracted and the row shows Ready to parse.
AI Parse — the blue sparkle button (enabled once text is extracted). The platform extracts typed requirements, status becomes Parsed, and the Extracted Requirements panel opens automatically.
Show / Hide requirements — the purple list button (enabled once parsed) toggles that same review panel.
Review each requirement — Approve, Reject, Edit, or Delete.

Only APPROVED requirements drive generation. Pending (AI-extracted) and rejected requirements are ignored by the generator.

Step 3 — Understand requirement types

Extracted requirements are typed. The review panel labels them with short chips: Field Constraint, Business Rule, State Transition, Cross-Field, Security, Performance, Data Format, Error Handling, and Integration — plus backlog-shaped types User Story, AC (acceptance criterion), Capability, and Use Case.

Notes and accuracy guardrails

gRPC is adapter-only. Rule Intelligence can extract facts from gRPC-related material, but the platform does not run gRPC at test time — never treat an extracted gRPC requirement as a runtime gRPC test.
Confidence gate. Any AI-extracted requirement below 70% confidence is flagged Needs review rather than treated as ready.
Re-parse is destructive. Re-parsing a document that already has approved or human-reviewed requirements wipes and replaces them; the platform shows a Re-parse will replace reviewed requirements confirmation first.
Two kinds of "data." Data for Generating Tests supplies sample inputs to the generator; data used when running tests lives elsewhere (Project Settings → Datasets).

Understanding Rule Intelligence