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Analysis

Turning Painstorming Into a Reliable AI Workflow

Are you tired of spending days browsing social media, painstakingly taking notes about what your customers want?

Painstorming is usually a messy, human-centric activity: gather frustrations, compare perspectives, extract patterns, and refine everything into a core problem worth solving. The concept is solid, but the execution tends to be improvised, inconsistent, and hard to repeat. With background in Formal Methods, We’ve been experimenting with a way to turn that process into something systematic. Something an AI system can help with without drifting into fantasy solutions or “this sounds plausible” hand-waving. What finally emerged is a recurrent workflow that uses AI in a very narrow, disciplined loop. It’s not a “creative” flow; it’s more like an evidence-gatherer that stays on rails.

The Core Idea

Instead of prompting a model for insights directly, the system repeatedly asks the model to:

1. Perform theory of mind analysis.

   LLMs have been shown to perform theory of mind analysis on par with humans.

2. Attach sourced quotes, snippets, and anchors that exibit pain-point emotions.

   Every assertion must point back to where it came from.

3. Compile a report from multiple refining iterations.

   Each cycle forces the model to compare previous outputs with the source material, eliminate speculative claims, and strengthen the mapping between problems and textual evidence.

This recurrent structure turns the model into a verifier, not a generator.

Why It Minimizes Hallucinations

The model never operates in a free-form conceptual mode. Each loop restricts it to:

• extracting only what already exists in the content,
• attaching verifiable proof,
• rejecting its own earlier unsupported assumptions.

A hallucination can still happen - no system is perfect, but the workflow makes it self-correcting. Unsupported items decay over successive rounds because the model must either justify them or discard them.

What Users Get

Instead of a list of “AI-sounding problems,” the result is a painstorming sheet anchored directly to the user’s own data:

• highlighted pain points with direct citations
• clusters of related frustrations
• repeated themes supported by underlying quotes
• a final distilled “problem map” the user can audit line by line

It’s transparent, inspectable, and easy to hand off to stakeholders who need to see *why* a conclusion was made rather than just the conclusion itself.

Why This Matters

Good painstorming isn’t about creativity - it’s about clarity, traceability, and pattern-finding. AI is perfectly suited for that kind of repetitive, citation-heavy extraction as long as it’s kept inside a disciplined loop. By condensing the broader painstorming literature into an automated, verifiable, multi-pass workflow, we end up with something rare in this space: AI-assisted analysis where every insight has a receipt.