I’ve been testing several assistants on practical, real-world cloud infrastructure questions. While they’re often great for generating a quick starting point, I’m increasingly concerned about subtle factual inaccuracies in their code snippets and configuration advice. These aren’t wild hallucinations, but rather outdated parameters, incorrect service names, or non-existent properties in Terraform or CloudFormation resources.
For example, last week I asked a leading model for a Terraform snippet to create an AWS EFS filesystem with lifecycle management. The generated code used the attribute `lifecycle_policy` within the `aws_efs_file_system` resource, which looks perfectly reasonable. However, in the actual AWS provider, `lifecycle_policy` is a separate resource (`aws_efs_file_system_policy`). A junior engineer might not catch this, and the error message isn’t always clear.
This raises my core question: **How are you all systematically benchmarking for this kind of factual accuracy?**
I’m looking for methodologies beyond simple “does it run?” checks. I’m thinking about:
* Maintaining a curated set of prompts targeting specific, version-sensitive services (e.g., “Create a GCP Cloud Run service with CPU throttling”).
* Comparing outputs against the actual, current official documentation—not just a syntactic validation.
* Tracking drift over time as APIs and providers update.
What’s your process? Do you run automated validation against real cloud providers’ APIs, or use a sandbox? I’m particularly interested in reproducible test cases for common IaaS and PaaS offerings.
mod team
Yeah, the EFS example hits close to home. I've run into the same with Azure Bicep modules where the API version in the generated code is deprecated. My semi-systematic approach is to pair the generated snippet with the official provider documentation in real-time. I'll have the docs for `aws_efs_file_system` open in another tab and literally cross-check each attribute.
For benchmarking, I started a simple spreadsheet tracking prompts against specific provider versions. The key column is whether the output matches the *current* official syntax, not just if it's plausible. It's manual, but it's shown me which models are better at keeping their cloud knowledge fresh. Claude seems to edge out others on newer Azure services, in my totally unscientific tests.
Your spreadsheet idea is solid, especially tracking against specific provider versions. That version pinning is crucial because a model's training cutoff might be just a few months before a major provider syntax change.
I've automated something similar using simple scripts. I take a batch of generated snippets and run `terraform validate` (or `cfn-lint` for CloudFormation) in a container with the pinned provider version installed. It catches structural issues quickly, but you're right that it can't flag "plausible but wrong" logic like a misplaced lifecycle policy.
Even with Claude's edge, I'd be wary of newer Azure services - their API versions and Bicep spec can change during preview phases, which models rarely track. My rule is to treat any generated snippet as a first draft that *must* pass through the exact docs for the version I'm locked to.
pagerduty certified lifer