I’ve been thinking about a problem that has become increasingly common for me while building AI applications:

When several AI models give different answers to an engineering question, what should actually determine which answer we trust?

For example, suppose I’m building a RAG system and ask:

“Should I use pgvector or Pinecone for this project?”

I can ask ChatGPT, another model, search documentation, read blog posts, and look at examples from other developers.

But I don’t think the difficult part is getting more answers.

The difficult part is deciding:

  • Which evidence is actually relevant to my constraints?

  • How should conflicting recommendations be handled?

  • How much confidence should I have in the final recommendation?

  • What information would change the recommendation?

  • And, most importantly, what should I actually do next?

I’ve been experimenting with a small system around this idea.

Instead of treating an LLM response as the final answer, the system tries to structure the process as:

Context → Evidence → Deliberation → Decision → Action → Reflection

One thing I found particularly interesting is that model agreement isn’t necessarily evidence of correctness. Multiple models can agree for the same wrong reason.

This made me wonder whether a useful AI engineering assistant should expose things like:

evidence quality, disagreement, uncertainty, trade-offs, and counterfactuals

rather than simply producing a confident answer.

I’ve also been using ChatGPT and Codex extensively while developing the experiment. ChatGPT has mainly been useful as a reasoning/review partner, while Codex has helped with implementation, debugging, refactoring, and verification.

That raised another question for me:

If AI is increasingly involved in making and implementing engineering decisions, how should we make those decisions easier for humans to inspect and challenge?

I’d really appreciate perspectives from developers here:

  1. Do you think this is actually a problem worth solving?

  2. Would exposing model disagreement make your engineering workflow better, or just slower?

  3. What signals would you want to see before trusting an AI-generated engineering recommendation?

  4. Are there existing tools or approaches that already solve this well?

I’ve been exploring these questions through a project called GenPHD. If anyone is interested in seeing the implementation or discussing the approach, I’m happy to share it.

I’m much more interested in hearing where this idea is wrong than in promoting the project.

Thanks!