AutoRAG: Automate RAG Evaluation
AutoRAG optimizes Retrieval-Augmented Generation by automating the evaluation and optimization process with AutoML-style automation, allowing founders to fine-tune their language generation models efficiently
- •When you need to improve the performance of your language generation models
- •When you want to automate the process of evaluating and optimizing Retrieval-Augmented Generation models
- •When you need to reduce the time and effort required to fine-tune your language generation models
python scripts/train.py --model_name_or_path t5-base --dataset_name wiki_text --do_train --do_eval --train_batch_size 16 --eval_batch_size 64
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/autorag && curl -fsSL https://workflowstacks.com/api/skills/autorag/claude-skill -o ~/.claude/skills/autorag/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
What's inside — free to inspect
Read the entire source before you build — unlike paid marketplaces that hide it behind a buy button.
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/autorag && curl -fsSL https://workflowstacks.com/api/skills/autorag/claude-skill -o ~/.claude/skills/autorag/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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