One of the biggest challenges in QA is maintaining the regression test suite.
Every time a requirement changes or a new one is added, you must modify the regression suite to ensure it remains aligned with the constantly changing requirements.
This is time-consuming for QA teams, who already have to design test cases for new functionality within tight deadlines.
It is also error-prone, as manual updates can easily miss edge cases or negative paths.
A common shortcut is to use large language models (LLMs) like ChatGPT to generate Gherkin scenarios by pasting requirements into a prompt.
While this approach can quickly produce tests for a single feature, it falls short in real-world projects.
A one-off LLM prompt has no memory of your requirement changes and doesn't link new functionality to regression risks. It also often ignores boundaries or negative flows since it doesn't understand the application context.
This is where context-awareness becomes important.
By storing past and present requirements in a way that preserves their meaning, you can guide an LLM to generate test cases. These test cases not only cover new functionality but also confirm that existing features still work as expected.
In this guide, you'll see how a context-aware framework works, why it goes beyond basic LLM approaches, and how you can apply it to keep your regression tests current with much less effort.
Traditional regression testing struggles because requirements change rapidly. Tests written yesterday can already be outdated by today's changes.
This forces testers to spend time rewriting cases instead of improving overall coverage.
That tedious manual work not only slows down delivery but also increases the chance of missing negative scenarios and edge cases.
Basic LLM-based test generation doesn't really solve this problem. When you paste requirements into a single prompt, the model generates scenarios in isolation. It overlooks older requirements, fails to connect new features with regression risks, and generates one-off test cases instead of an evolving suite.
For example, an LLM might generate a checkout scenario for a new "guest user" feature, but it won't automatically remind you to retest existing "registered user" flows.
Context-awareness changes this dynamic.
By semantically storing requirements, the system understands that concepts like "login" and "authentication" are related, even if they are worded differently.
When new requirements are introduced, it can automatically detect changes, highlight the affected areas, and generate new test cases, ensuring that existing workflows remain covered.
This allows you to keep your regression packs in sync with product changes rather than constantly trying to catch up later.
Context-aware test generation doesn't replace testers. It functions as an AI assistant that automates the repetitive tasks associated with regression maintenance.
You still decide testing priorities and strategy, but you don't waste hours rewriting scenarios by hand.
A context-aware test generation framework typically follows four steps: storing requirements with meaning, spotting changes, generating test cases, and validating format.
Each step combines conceptual understanding with practical tools, allowing you to transition from theory to implementation without feeling overwhelmed.
Instead of saving requirements as plain text, you store them in a vector database such as ChromaDB.
Vectorization captures the semantic meaning of sentences, not just their keywords. This means that if one document mentions "login" and another mentions "authentication," the system recognizes they describe related concepts.
To make this effective, requirements are broken into smaller, meaningful chunks by feature or module. These are ingested from common formats, such as .txt, .md, or .pdf.
You can improve the framework to add more file type support.
When a new requirement is added, the system compares it against existing requirements using embeddings such as BAAI/bge-small-en-v1.5. This functions like an automated impact analysis.
The embedding search identifies related features and highlights which parts of your system may be affected.
Now, instead of manually scanning requirement docs to figure out what's changed, you instantly see where regression tests are needed.
Once changes are identified, an LLM such as LLaMA drafts new Gherkin test cases.
These cover positive paths, negative scenarios, boundary conditions, and regression risks. Using Cloudflare's inference layer also means that requirement documents are not retained. This addresses privacy concerns that come with sending sensitive project details to public GPTs.
Tests will generate automatically, but you still need to review them for accuracy and business alignment.
Finally, a lightweight syntax check ensures that generated test cases follow proper Gherkin formatting. This keeps scenarios clear for both technical and non-technical stakeholders, preventing malformed steps from creating bottlenecks during execution.
The combination of semantic storage, automated impact detection, AI-driven test generation, and format validation yields regression packs that remain up to date without requiring heavy manual rework.
Imagine your e-commerce platform originally required users to register before checkout. A new requirement now allows guest checkout with only an email and shipping address.
Let's check what one-off LLM prompts like ChatGPT generate with the following prompt:
Prompt:
Write test cases for an e-commerce platform. Guests are allowed to check out with only an email and a shipping address.
TC-001: Guest checkout with a valid email and shipping address
TC-002: Guest checkout with apartment/unit details in address
TC-003: Guest checkout with international address
TC-004: Guest checkout with different shipping and billing addresses
TC-005: Guest checkout with invalid email format
TC-006: Guest checkout with missing required shipping address fields
TC-007: Guest checkout with unsupported characters in address
TC-008: Guest checkout with excessively long email or address
TC-009: Guest checkout triggers confirmation email
TC-010: Guest checkout with existing registered user's email
TC-011: Order history for guest
TC-012: Payment integration check
TC-013: Guest clicks back button before confirmation page
Now let's try with a context-aware approach.
In addition to the regular test cases above, the context-aware test case generator will generate the following additional test cases to cover regression scenarios.
Feature: Existing Account Creation and Login Process
Scenario: Existing User Can Still Create Account
Scenario: Existing User Can Still Log In
What makes these scenarios stronger than a basic LLM output is the regression awareness.
A simple one-off LLM prompt might generate only the new guest checkout flow, but the context-aware approach ensures you also test invalid inputs and confirm that the existing registered checkout still works.
This balance of new coverage, negative paths, and regression protection gives teams far more confidence in their updates.
To get the most out of context-aware test generation, you need to follow some best practices.
By following these practices, you ensure that AI-assisted test generation strengthens your process rather than introducing new risks.
Keeping regression tests aligned with changing requirements remains one of the most challenging tasks for QA teams.
Manual updates consume valuable time, while basic LLM prompts often generate isolated scenarios with no awareness of regression risks.
The result is either outdated test packs or incomplete coverage, which compromises product quality.
Context-awareness breaks this cycle.
Try this concept on your own project. Take a recent requirement change and compare your manual updates with AI-generated Gherkin scenarios.
You'll quickly see the time savings and coverage improvements that context-awareness provides.
Over time, you can integrate this approach into your workflows, starting with lightweight scripts, then expanding into deeper integration with your test management tools.
To see the framework in action, you can explore the GitHub repository. All the required technical information to set up the framework is mentioned in the ReadMe file.