GPT 4 IaC

Date: 2025-02-12

I’ve made some practical experiments with GPT in the IaC context and gave a speech about that at the meetup.

I’m using chatGPT like solutions in real life for different purpose:

• Read laws/documents and share the result.
• Write “official” letters in foreign languages (Spanish/Catalan).
• Translate words/abbreviations with multiple meanings.
• Analyze medical historical data and provide forecasts.

And I realized 🔥 it works well. But what’s about the IaC? is it usefull?

Limitations

There are many solutions available publicly:

• Phind
• Gemini
• Perplexity
• ChatGPT
• There’s an AI for That
• GigaChat
• Claude
• DeepL

However, I’ve decided to limit myself to Copilot because it integrates smoothly with VSCode and is allowed for use. The main limitation is that only public data is allowed.

Usecases

@ work: Documentations & presentations

Goal: Fluent, easy-to-read texts without mistakes.

Pros:

• Can rephrase and increase readability.
• Can propose reasonable grammar and syntax changes.
• Applicable for presentations as code.

Cons:

• None identified

Conclusion: 🔥 it works well.

@ work: create ansible lookup plugin

Goal: Transform the script get-latest-version.py to an Ansible lookup plugin. The purpose of the script was to:

• Connect to a Maven repository.
• Search for the artifact according to some rules.
• Print the artifact version.

Pros:

• Can generate the boilerplate but not ideal.
• Can propose reasonable syntax changes.

Cons:

• Suggested fixes can have wrong logic or indentation.

Conclusion: ✅ ok to use for well known domain area.

@ work: generate documentation

Goal: Avoid boring tasks.

Pros:

• Can generate human-friendly, readable documentation.

Cons:

• Suggested fixes can have wrong logic or indentation.

Conclusion: ✅ it works well.

@ work: explain jinja2 expressions

Goal: Understand written templates.

Pros:

• Explain step by step what’s happening.

Cons:

• The context is required.

Conclusion: ✅ ok to use for well known domain area.

@ work: debug Ansible OTC dynamic inventory plugin

Goal: Get list of VMs from OTC in Ansible friendly format.

Pros:

• Can generate suitable config.

Cons:

• Not enough knowledge about OTC and suggests non-existent parameters.
• Unable to identify the root cause of connection failures.
• It is very easy to send secrets to third parties.

Conclusion: ⛔️ fail.

@ work: fix errors in Ansible roles

Goal: Fix errors during Java installation.

Pros:

• It proposed correct changes.

Cons:

• Some iterations are required.
• It doesn’t know about infrastructure.

Conclusion: ❓ acceptable.

@ work: explain dependencies across the project

Goal: Understand infrastructure dependencies across the different parts.

Pros:

• Not identified.

Cons:

• Doesn’t know the entire project context.
• It doesn’t support whole project as a context.

Conclusion: ⛔️ doesn’t work.

Copilot 4: summary

Conclusion: ❗️just imagine that there is very smart junior in your team.

Ideas

In case of IaC I’m following IDLC(SDLC for IaC) approach and I think it can be improved.

💡 Idea: PR reviewer

Make GPT as an optional reviewer in a repo:

1. Get the diff.
2. Load affected files as context.
3. Provide prompt: “review it”.
4. Suggest changes to PR.

Conclusion: ❗️ IDLC can be improved.

💡 Idea: Increase IaC test coverage

There is IaC testing pyramid concept. It describes how to test IaC. The problem is that it’s slow or just linting. The idea is that maybe it will be possible to add gpt to Static Analysis level. I.e. simulate an ansible or terraform execution without real execution. Maybe it will be faster or cheaper.

Conclusion: ❗️ IaC testing can be improved.

GPT 4 IaC summary

• ❗️ Just imagine that there is very smart junior in your team.
• 🔥 Documentations & presentations.
• ✅ Create Ansible lookup plugin.
• ✅ Generate documentation.
• ✅ Explain Jinja2 expressions.
• ❓ Fix errors in Ansible roles.
• ⛔️ Debug Ansible OTC dynamic inventory plugin.
• ⛔️ Explain dependencies across the project.
• 💡 PR reviewer.
• 💡 Increase IaC test coverage.