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On the recordRead 11 Oct 202621 facts
Pythia
On the record
- Backends
- The repository README says Pythia includes Gemini and Ollama backends and can use another backend whose LLM is called with an .invoke() method.github.com · read 5 Oct 2026
- Clinical focus
- The project describes its optimization as clinical and sensitive to sensitivity and specificity.clai-group.github.io · read 11 Oct 2026
- Configurable thresholds
- The README says sensitivity and specificity thresholds are configurable and default to 0.75, while the priority metric defaults to specificity.github.com · read 5 Oct 2026
- Data requirements
- The documented workflow requires development and validation datasets, an output directory, an SOP, and an initial prompt.github.com · read 11 Oct 2026
- Dataset format
- The README recommends CSV datasets, with each CSV representing a person and rows representing visits or notes, using “visit” and “Ground Truth” columns.github.com · read 5 Oct 2026
- Error analysis
- A specialist LLM analyzes failures in aggregate, identifies root causes, and proposes improvement hypotheses.clai-group.github.io · read 11 Oct 2026
- Evaluation
- Pythia evaluates prompts against a held-out dataset using configurable metrics and tracks results across iterations.clai-group.github.io · read 11 Oct 2026
- Framework
- The site says Pythia uses LangGraph stateful agentic workflows.clai-group.github.io · read 5 Oct 2026
- History and export
- Pythia logs prompt versions, scores, and controller decisions, and the site says users can compare runs, export results, and roll back to earlier checkpoints.clai-group.github.io · read 5 Oct 2026
- Hosting and privacy
- The project describes Pythia as locally hosted and privacy-preserving, with optimization running on open-source models within the institution.clai-group.github.io · read 11 Oct 2026
- Intended users
- The project presents Pythia for clinical prompt optimization and also gives a general example involving home-buying inquiries.github.com · read 11 Oct 2026
- Iteration control
- A controller can continue, backtrack to a stronger prior prompt, or halt when the prompt has converged.clai-group.github.io · read 11 Oct 2026
- Local hosting
- The maker describes Pythia as locally hosted and privacy-preserving, with optimization running on open-source models within the institution.clai-group.github.io · read 5 Oct 2026
- Model backends
- The repository says Pythia includes Gemini and Ollama backends and can use another LLM backend that provides an invoke() method.github.com · read 11 Oct 2026
- Model scale
- The site says optimization can run on models ranging from one billion to one trillion parameters.clai-group.github.io · read 5 Oct 2026
- Prompt improvements
- An improver agent generates and ranks multiple candidate prompt rewrites before evaluation.clai-group.github.io · read 11 Oct 2026
- Purpose
- Pythia is an automated prompt optimization engine that iteratively evaluates prompts, analyzes failures, and synthesizes improvements.clai-group.github.io · read 11 Oct 2026
- Run history
- Pythia logs prompt versions, scores, and controller decisions, and supports comparing runs, exporting results, and rolling back to checkpoints.clai-group.github.io · read 11 Oct 2026
