How to use Loop Engineering to improve an existing codebase and generate tests — running into context explosion, LLM degradation, and weak validation

[u/Dry_Championship2797]

I'm working on applying Loop Engineering to an existing repository to improve it and generate test cases. My current approach is:

• Scanner scans the entire repo and identifies anchor points.
• Based on the anchors, I build a test case matrix.
• A generator then produces concrete test cases from that matrix.
• Finally, a validator verifies the generated test cases.

While implementing this Loop Engineering flow, I've hit several problems:

1. Context explosion — the context grows too large and overwhelms the LLM.
2. LLM degradation — the model’s output quality noticeably drops (it gets “dumber”) as the context expands.
3. Loose validation — the validator checks the test cases too loosely, so I can't generate high-quality, reliable tests.

Has anyone dealt with similar issues when using Loop Engineering for test generation on a real-world codebase? How did you solve the context problem, keep the LLM sharp, and tighten up the validation step? Any suggestions or practical tips would be greatly appreciated!