mshumer/gpt-prompt-engineer - GitHub vs H2O.ai

mshumer/gpt-prompt-engineer is an open source prompt engineering toolkit that generates, tests, and ranks candidate prompts for a task you define. You describe the use case, supply test cases, and the notebooks create multiple prompt variants, run them against every test case, and sort results with an ELO rating system starting at 1200. It ships as Jupyter notebooks you can run in Google Colab or locally.

Most prompt tools help you write one prompt at a time. gpt-prompt-engineer treats prompt selection like a tournament: dozens of candidates compete on your test cases, and the highest ELO scores surface the winners. Separate notebooks cover classification tasks, Claude 3 Opus with auto-generated test cases, and Opus-to-Haiku conversion for cheaper inference. Optional Weights & Biases and Portkey logging trace each run.

ML engineers, prompt engineers, and AI developers use it when they need reproducible prompt tuning instead of manual trial and error. The repo has 9.7k GitHub stars and runs on your own OpenAI or Anthropic API keys. It is free under the MIT license.

H2O.ai builds enterprise machine learning and generative AI software for banks, telcos, and government teams that need models on private data. The stack spans open-source H2O-3, AutoML in Driverless AI, no-code deep learning in Hydrogen Torch, and h2oGPTe agents that run on-premises, in VPCs, or on H2O-managed cloud.

Where many vendors push a single chatbot SKU, H2O.ai ships separate paths for predictive modeling, LLM fine-tuning in Enterprise LLM Studio, tabular predictions through tabH2O CSV uploads, and vertical agents for fraud, call centers, and document workflows. Case studies cite Commonwealth Bank cutting scam losses 70% and AT&T reporting 2x ROI on generative AI spend in call center automation.

Data science leaders and regulated enterprises adopt it for air-gapped deployments with SOC 2 Type II plus HIPAA/HITECH coverage on H2O AI Cloud. The platform lists integrations with Google Drive, Slack, GitHub, AWS, Snowflake, and SharePoint, plus open-weight models like Danube3 SLMs and Mississippi vision-language OCR.