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Custom prompt shortcut / Software Development

/databaseoptimization — ChatGPT Prompt Guide

Explore the /databaseoptimization custom ChatGPT prompt shortcut with a practical template, examples, and review steps. Learn how to refine outputs for your workflow.

What does /databaseoptimization do?

Purpose: Investigate a slow orders dashboard query using query plans.

This is a custom prompt convention, not a built-in or official ChatGPT slash command. Entering the slash text alone does not guarantee that ChatGPT will invoke a special tool. Use the complete structured prompt below or configure your own app to interpret this shortcut.

Key decision for this workflow

Check predicate selectivity, row estimates, sort operations, and whether an index matches the WHERE and ORDER BY clauses.

Copy-ready task brief

/databaseoptimization Objective: Investigate a slow orders dashboard query using query plans. Inputs: SQL query, EXPLAIN output, table cardinalities, indexes, and latency samples. Deliverable: A before/after execution-plan comparison and index proposal.

Reusable prompt template

/databaseoptimization Objective: Investigate a slow orders dashboard query using query plans. Available materials: SQL query, EXPLAIN output, table cardinalities, indexes, and latency samples. Required output: A before/after execution-plan comparison and index proposal. Acceptance test: Check predicate selectivity, row estimates, sort operations, and whether an index matches the WHERE and ORDER BY clauses. Boundary: An index can speed reads but increase writes and storage; benchmark representative production-like data. Return the requested deliverable first, then a brief verification checklist and any unresolved questions.

How to use this shortcut

  1. Collect the source inputs: SQL query, EXPLAIN output, table cardinalities, indexes, and latency samples.
  2. Run /databaseoptimization with the copy-ready task brief above.
  3. Request the concrete output: A before/after execution-plan comparison and index proposal.
  4. Check the result: Check predicate selectivity, row estimates, sort operations, and whether an index matches the WHERE and ORDER BY clauses.

Quality checks and limitations

Acceptance criteria: Check predicate selectivity, row estimates, sort operations, and whether an index matches the WHERE and ORDER BY clauses. Limitation: An index can speed reads but increase writes and storage; benchmark representative production-like data.

Related tools and learning

Continue with the 35 prompt generators, the technical AI Guides, and the Image Guides for deeper examples.

/code/debug/refactor/codeaudit/prompttesting/promptbenchmark

Worked example: Investigate a slow orders dashboard query

Illustrative output: Inspect EXPLAIN for orders filtered by customer_id and created_at; compare index options and measured timings.

Review and limitations

Benchmark on representative data; account for write overhead and cardinality.

Field workflow: inputs, decisions, and validation

When to use this shortcut: Investigate a slow orders dashboard query using query plans.

What to provide: SQL query, EXPLAIN output, table cardinalities, indexes, and latency samples.

What to request: A before/after execution-plan comparison and index proposal.

Practical method: Check predicate selectivity, row estimates, sort operations, and whether an index matches the WHERE and ORDER BY clauses.

Important limitation: An index can speed reads but increase writes and storage; benchmark representative production-like data.

Frequently asked questions

Is /databaseoptimization an official ChatGPT command? No. It is a reusable custom shorthand from this catalog. Does it work without a plugin? You can copy the full prompt into an assistant; automatic slash-command routing requires application logic. Can the output be trusted without review? No. Review accuracy, safety, and suitability before use.