# mshumer/gpt-prompt-engineer

Repository: https://github.com/mshumer/gpt-prompt-engineer
Canonical: https://ross.abutalabs.com/products/gpt-prompt-engineer
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2025-10-16T18:52:42+00:00

## Health v2 (maintenance only)
Score: 50/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 47, release rhythm 35, longevity 82
- inputs: {"age_days": 1156, "days_push": 321, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9675, forks 681 (observed 2026-08-28T04:10:36.694161+00:00)

## What it is
A Jupyter Notebook-based tool that automatically generates, tests, and ranks LLM prompts for a given task using GPT-4, GPT-3.5-Turbo, or Claude 3 Opus. It evaluates candidate prompts against user-provided test cases and ranks them with an ELO rating system to surface the best performers.

## Use cases
- find the best prompt for my llm task
- automatically generate and rank prompts
- optimize system prompts with test cases
- evaluate prompts with an elo rating system
- convert expensive model outputs to a cheaper model
- auto-generate test cases for prompt evaluation

## When to choose
- you want to experimentally compare many prompt variants against test cases
- you work in notebooks and want a quick, low-setup prompt optimization workflow
- you want to distill a strong expensive model's behavior into a cheaper faster model

## When to avoid
- you need a production-grade prompt management or versioning platform
- you want a GUI or CLI tool rather than running notebooks
- your evaluation requires human review or domain-specific metrics beyond pairwise comparison

## Facets
- artifact type: library
- maturity: active
- function: prompt-engineering, llm-inference, benchmarking, testing
- domain: large-language-models, artificial-intelligence, developer-tools
- platform: python
- tags: jupyter-notebook, elo-rating, prompt-optimization, openai, anthropic-claude, google-colab, web-server

## Member repositories
- mshumer/gpt-prompt-engineer (main) score 50

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:36.694161+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:21:17.829131+00:00, confidence not recorded.
  - readme: https://github.com/mshumer/gpt-prompt-engineer (fetched 2026-08-28T04:10:36.694161+00:00, sha ad982656a326)
- Data as of 2026-08-30T08:39:29.467469+00:00.