public

The Postgres query tuning prompt I actually kept

I kept the useful constraints and dropped the role-play. It works better when the missing details are stated plainly.

Engineeringgpt-5#chatgpt #coding #performance #postgres

Anika Kim

Help me read an EXPLAIN plan and choose the smallest index or query change that matters.

Context:

Goal: [what a good result lets me do]
Current situation: [paste notes, draft, data, or constraints]
Audience: [who will use the result]
Deadline or limit: [time, budget, tools, length]

Before answering, ask up to three questions only if the answers would materially change your recommendation. Otherwise, state your assumptions and continue.

Give me:

A practical first pass I can use now
The main trade-offs or risks
The smallest check or test to run next
What information would change your answer

If a fact is missing, label the gap instead of inventing a detail. Use direct language. Keep the result specific to the context I provide.

ChatGPTgpt-5

This is usable as written. The important parts are the definition of done, the instruction to expose assumptions, and the request for a smallest test. For Postgres query tuning, I would start with these checks:

Reproduce the failure before changing code
Keep the patch smaller than the diagnosis
Write the rollback before deployment

If you share real material, remove anything private first and replace bracketed fields with concrete details.

Anika Kim

That is the version I kept. The three-question cap stopped the setup from turning into an interview.

4 comments

Miles Rivera@milesrivera·3mo ago
Tried the smaller-test idea on Postgres query tuning. It exposed the bad assumption pretty quickly.
- Caleb Bose@calebbose·3mo ago
  Same here. The first question was doing too much work.
Amara Ross@amaraross·3mo ago
This is close to how I handle Postgres query tuning, especially the bit about marking unknowns.
Anika Brooks@anikabrooks·2w ago
This is much easier to reuse than a long list of general advice.