One post tagged with "feedback"

While the world focuses on ai-coding agents, the true impact of Ai on solo engineers is not only faster code generation but the automation and compression of feedback loops.

We're going to see a new class of tools which raise the bar of our ability to explore and validate ideas then iterate on them at speeds previously reserved for teams.

These tools will partially automate the operational processe currently undertaken by product managers in the enterprise world. In those larger teams, it'll free up those PMs to the more valuable work of talking to stakeholders and speeding up decisions. But for solo engineers and small teams it has the ability to transform thought processes, opening up a world in which they can make more informed speedy decisions.

For example the highly valuable but work intensive process of collecting and evaluating user feedback into something which can easily be used for roadmap analysis and prioritisation.

Below we'll present a system which you can set up in under an hour to deliver reliable reports for your prioritisation sessions.

Why collect feedback​

Collecting feedback is not the hard part. The hard part is keeping it usable over time.

Common failure modes:

• Feedback is scattered across tools and impossible to review in one place.
• Important patterns are hidden because every note is free-form.
• Decisions are made from memory, not from a stable historical record.

What we want instead:

• One lightweight system for capturing raw input continuously.
• A repeatable process for converting text into tags and themes.
• Outputs that both humans and LLMs can consume directly.

Turning feedback into something useful​

The pipeline:

1. Collect free-form feedback in Tally.so.
2. Pull submissions directly from the Tally API to a csv in your git repo.
3. Produce structured outputs (tagged-feedback.csv + weekly summary markdown).

Core fields to generate per entry:

• feedback (free text field)
• email (some field to tie the feedback to a user)
• theme (UX, bug, feature request, confusion)
• tags (LLM generated tags)
• sentiment (positive, neutral, negative)
• urgency (low, medium, high)
• repeat_signal (single mention vs repeated pattern)

Output artifacts:

• reports/weekly-feedback-summary.md for fast human review.
• reports/tagged-feedback.csv for filtering, trend checks, and LLM context windows.

The free 30min setup​

Tool stack (all low-cost or free)​

• Tally.so for intake form.
• GitHub repo for versioned CSV snapshots and reports.
• Small Python script using an LLM call for tagging.
• Optional cron job or GitHub Actions for weekly runs.

Setup checklist​

1. Create a short feedback form (what happened, what they expected, impact).
2. Add hidden fields to your form (email, theme, tags, sentiment, urgency, repeat_signal)
3. Create a Tally API key and keep it in your environment (for example TALLY_API_KEY).
4. Capture your formId from Tally.
5. Run the Python script download_feedback.py to pull submissions into a local CSV snapshot
6. Run tag_feedback.py to classify each row by theme/sentiment/urgency using the Claude Code CLI
7. Run generate_report.py to produce a weekly markdown summary
8. Commit generated outputs to keep an auditable product feedback history.

What this unlocks immediately​

• Faster weekly review loops without manual sorting.
• Better product conversations grounded in tagged evidence.
• Reusable context for AI agents working on roadmap or implementation decisions.

Closing​

You do not need a PM org or expensive tooling to get real signal from feedback. You need a simple, repeatable pipeline that turns raw text into structured inputs you can act on.