# YiVal/YiVal

Your Automatic Prompt Engineering Assistant for GenAI Applications

Repository: https://github.com/YiVal/YiVal
Canonical: https://ross.abutalabs.com/products/yival
Homepage: https://yival.io/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: ai, prompt, llm, ai-experiments, ai-toolkit, promptengineering, aigc, generative-ai, prompt-engineering, fine-tuning, prompt-tuning, api, autogpt, framework, gpt4, midjourney, python, stable-diffusion, auto-prompting
Last push: 2024-04-22T04:42:25+00:00

## Health v2 (maintenance only)
Score: 19/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 81
- inputs: {"age_days": 1146, "days_push": 863, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2133, forks 329 (observed 2026-08-28T04:06:17.317513+00:00)

## What it is
YiVal is an open-source Python framework that automatically tunes prompts, RAG configurations, and model parameters for generative AI applications using a data-driven, evaluation-centric approach. It replaces manual prompt iteration with automated experiments that optimize for quality, latency, and inference cost.

## Use cases
- automatically improve my LLM prompts instead of guessing
- evaluate and compare different prompt variants for my GenAI app
- tune RAG configuration parameters with real metrics
- reduce GPT-4 inference costs by finding cheaper model configs with similar quality
- run A/B experiments on prompt engineering for my chatbot
- find the right metrics and evaluators for my LLM use case
- fine-tune model parameters without deep ML expertise

## When to choose
- you are building LLM or GenAI applications and want data-driven prompt optimization
- you need systematic evaluation of prompts, RAG settings, or model configs
- you want to cut inference costs while maintaining output quality
- you prefer a Python framework with notebook tutorials and Docker deployment

## When to avoid
- you need a production-hardened, actively maintained tool - the project appears to have stalled after early 2024
- you only need simple one-off prompt testing without evaluation pipelines
- your stack is not Python-based
- you need a managed commercial platform rather than a self-run framework

## Facets
- artifact type: framework
- maturity: experimental
- function: prompt-engineering, machine-learning, llm-inference, rag, benchmarking, testing, llm-training
- domain: large-language-models, artificial-intelligence, machine-learning, developer-tools
- platform: python, cross-platform, cli
- tags: prompt-tuning, auto-prompting, genai, evaluation, llm-ops, experiment-framework, aigc, retrieval-augmented-generation, docker

## Member repositories
- YiVal/YiVal (main) score 19

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:17.317513+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 2026-08-30T02:51:57.794681+00:00, confidence not recorded.
  - readme: https://github.com/YiVal/YiVal (fetched 2026-08-28T04:06:17.317513+00:00, sha e9058760f661)
  - homepage: https://yival.io/ (fetched 2026-08-29T10:32:03.063596+00:00, sha 7445a33ee87f)
- Data as of 2026-08-30T08:39:29.467469+00:00.