Eladlev/AutoPrompt

Health v2 · maintenance only

Adoption not part of the score

3010 stars · 265 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

AutoPrompt is a Python framework for optimizing LLM prompts using Intent-based Prompt Calibration. It iteratively generates challenging edge-case datasets, annotates them, and refines prompts to produce robust, production-grade prompts with measured accuracy.

Use cases

• optimize and refine LLM prompts automatically
• generate synthetic datasets of challenging edge cases for prompt evaluation
• benchmark production-grade prompts with minimal annotation effort
• migrate prompts between LLM models
• build robust moderation or classification prompts
• create ranker prompts for content generation tasks

When to choose

• you need reliable, robust prompts for real-world LLM applications
• manual prompt engineering is too time-consuming or error-prone
• you want to build evaluation benchmarks with minimal labeled data
• you need synthetic data generation combined with prompt optimization

When to avoid

• you need a simple one-off prompt tweak without iterative optimization
• your project has no budget for LLM API calls during calibration
• you require fine-tuning of model weights rather than prompts

Facets

framework · maturity active

prompt-engineering machine-learning data-generation llm-training large-language-models artificial-intelligence machine-learning developer-tools python cross-platform prompt-optimization prompt-tuning synthetic-data llm-evaluation intent-based-prompt-calibration

Member repositories

For agents

markdown · JSON · MCP: product_card(name="Eladlev/AutoPrompt")