gpt-prompt-engineer

What is gpt-prompt-engineer?

gpt-prompt-engineer is an open source project by Matt Shumer that automates the trial and error of prompt writing. You describe the task and supply a handful of test cases. The notebook then asks a language model to draft many candidate prompts, runs each one against every test case, and has the prompts compete against each other. Results are ranked with an ELO rating system in which every prompt begins at 1200, so the highest-rated prompt in the final table is the one that performed best. It runs in Google Colab or a local Jupyter notebook using your own OpenAI or Anthropic API key. Separate notebooks cover classification tasks, a Claude 3 Opus version that generates its own test cases, and a conversion flow that moves from Opus to Haiku for lower cost. Prompt engineers and developers use it to compare prompt wording on real inputs. The code is MIT licensed and free, and you pay only for the model calls.

Use cases

• Generate candidate prompts for a task
• Rank prompts with ELO ratings
• Test prompts against sample inputs
• Score prompts for classification tasks
• Cut cost by moving Opus output to Haiku
• Log prompt experiments to Weights & Biases

Who it's for

• Prompt engineers
• AI developers
• Researchers
• Data scientists

Why use gpt-prompt-engineer

• Replaces guesswork with a ranked table, so you can see which wording performs best on your own test cases.
• Free MIT-licensed code with no subscription, apart from the API usage you generate.
• Several notebooks cover open-ended generation, classification and Claude 3 workflows in one repo.
• Optional logging keeps temperature, token limits, prompts and final ratings for later comparison.
• The Opus to Haiku notebook helps keep the quality of a large model while lowering cost per generation.

gpt-prompt-engineer features

• Prompt generation from a task description and test cases
• Head-to-head testing of each prompt on all test cases
• ELO rating system that starts every prompt at 1200
• Classification notebook that scores prompts against true or false answers
• Claude 3 Opus notebook with auto-generated test cases
• Support for multiple input variables in the Claude 3 version
• Opus to Haiku conversion notebook for faster, cheaper generation
• Optional Weights & Biases logging of configs and final ratings
• Optional Portkey tracing of prompt chains

gpt-prompt-engineer integrations

• OpenAI
• Anthropic
• Weights & Biases
• Portkey
• Google Colab

How to get started with gpt-prompt-engineer

1. Open the notebook in Google Colab or a local Jupyter environment.
2. Paste your OpenAI API key, or an Anthropic key for the Claude 3 version.
3. Write a short description of the task and a list of test cases.
4. Choose how many prompts to generate and call the generate function.
5. Read the final table of ELO ratings, sorted from highest to lowest, and pick the top prompt.

gpt-prompt-engineer limitations

• You need your own OpenAI or Anthropic API key and pay for every model call
• Generating many prompts can get expensive, so the README suggests starting with about 10
• Runs as notebooks you edit and run yourself, with no hosted interface

gpt-prompt-engineer pricing

gpt-prompt-engineer is open source, so you can use and self-host it for free.

Prices can change and may vary by country or exclude tax. Check the official pricing page.

gpt-prompt-engineer alternatives

The main gpt-prompt-engineer alternatives listed on Debuly are Promptfoo, PromptLayer, Helicone, Braintrust, Evidently AI and OpenLIT. The table compares pricing and platforms; order is A–Z.

1. 

   Braintrust

   AI observability and evaluation platform for tracing LLM apps and agents, scoring outputs and catching regressions before release.

   Developer platformsCoding
   FreemiumVisit Braintrust (official site)
2. 

   Evidently AI

   Open-source Python library and platform to evaluate, test and monitor LLM apps, RAG systems, agents and ML models.

   Developer platformsCodingAutomation and agents
   Open sourceVisit Evidently AI (official site)
3. 

   Helicone

   Open-source LLM observability and AI gateway that logs requests, tracks cost and latency, and routes to 100+ models through one API.

   Developer platformsCoding
   FreemiumVisit Helicone (official site)
4. 

   OpenLIT

   Open-source, OpenTelemetry-native platform for tracing, evaluating and guarding AI agents, with cost and GPU monitoring

   Developer platformsCodingAutomation and agents
   Open sourceVisit OpenLIT (official site)
5. 

   Promptfoo

   Open-source LLM evaluation and red-teaming tool that finds prompt injections, jailbreaks and data leaks in AI apps and agents.

   Developer platformsCodingAutomation and agents
   Open sourceVisit Promptfoo (official site)
6. 

   PromptLayer

   Prompt management, evals and LLM observability so engineers and domain experts can ship prompt changes together.

   Developer platformsAutomation and agentsProductivity
   FreemiumVisit PromptLayer (official site)

gpt-prompt-engineer FAQ

Sources & verification

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