Vansah Contextual AI is designed to help QA teams generate high-quality, reviewable, and traceable test cases by understanding real project context, not isolated prompts.

This article explains when and why you should use Contextual AI, how it differs from traditional AI test generation, and how it fits into enterprise-scale quality engineering workflows.

What Does “Contextual” Actually Mean?

“Contextual” means Vansah Intelligence does not guess.

Instead, it evaluates:

Where the test is being generated (folder hierarchy)
Why it exists (linked requirements)
What constraints apply (folder/project instructions)
What evidence supports it (artefacts and specifications)

Each layer of context narrows ambiguity and increases precision.

What Problem Does Vansah Contextual AI Solve?

Traditional AI test generation tools typically rely on:

Single prompts or short descriptions
Generic templates
Isolated user stories without surrounding context

This often results in:

Shallow or repetitive test cases
Missed edge cases and dependencies
Assumptions or “AI guesswork”
Poor traceability for audits and compliance

How Vansah Contextual AI Is Different

Vansah Contextual AI is context-grounded. Instead of generating tests from a single input, it evaluates the full requirement ecosystem, including:

Folder hierarchy (L1–L5)
Folder descriptions and inherited context
Linked Jira work items (Epics, Stories, Tasks, Bugs)
Supporting artefacts (documents, PDFs, specifications)
Existing Test Cases (to avoid duplication)

This allows the AI to generate test cases that reflect how the system actually works, not how it is guessed to work.

When Should You Use Contextual AI?

Contextual AI delivers the most value in complex, real-world testing scenarios.

1. Complex Requirement Hierarchies

Use Contextual AI when requirements span multiple levels
Business processes broken into functional modules

The AI understands parent-child relationships and inherits context across folder levels, ensuring test cases align with the full requirement intent.

2. Enterprise or Regulated Environments

Contextual AI is ideal when:

Traceability is mandatory
Test assets must be audit-ready
Requirements evolve frequently

Because test cases are generated from linked Jira Work items and documents/artefacts, reviewers can clearly see why a test exists and what requirement it validates.

3. Large-Scale Test Repositories

When managing:

Hundreds or thousands of test cases
Multiple teams or projects
Shared services across programs

Contextual AI helps:

Reduce duplication
Maintain consistency across folders
Scale test creation without sacrificing quality

Example Workflow

A typical enterprise workflow using Contextual AI:

Organize requirements into Vansah folders (L1–L5)
Add clear folder descriptions at each level. This can be generated by Vansah or user created. Regardless ensure your Test Folder contains extra information that is required.
Link Jira work items (Epics, Stories, Tasks)
Attach supporting artefacts (specs, process docs)
Enable Contextual AI generation
Review AI-generated test cases
Refine and approve tests as part of QA governance

The AI continuously respects folder inheritance and linked context during generation.