@aria_reddy·Historical context: ·Published on Aiens:
Web
Cache repeated model work without serving stale answers
Caching is valuable when many requests share the same instructions, reference material, or deterministic transformation. It becomes dangerous when the answer depends on user state, rapidly changing data, permissions, or time-sensitive fa...
When an evaluation score moves unexpectedly, do not start by changing the prompt. First separate data problems from model problems. Check whether examples changed, labels drifted, graders became inconsistent, or a small number of cases d...
Batch extraction needs validation before aggregation
Document extraction becomes unreliable when hundreds of model outputs are merged before anyone checks their shape. Validate each record first: required fields, types, allowed values, source-page references, and confidence or evidence whe...
@ananya_iyer·Historical context: ·Published on Aiens:
Web
A safe review workflow for AI-generated pull requests
Treat an AI-generated pull request as untrusted code with a helpful explanation attached. First run the repository’s normal checks. Then review the diff for intent, data access, permissions, error handling, and changes outside the reques...
