🚨 It’s 2025 and your RAG is burning time and money

What if you could make it 30× faster and cheaper?

❌ Where RAG breaks down

↳ Retrieved passages are often irrelevant

↳ Longer contexts slow everything down

↳ Memory costs explode as you scale

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That’s where ReFrag comes in.

It rethinks how context is handled without sacrificing accuracy.

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🛠️ How ReFrag works

↳ Compresses text into chunk-level embeddings

↳ Precomputes once per chunk → reuse every query

↳ Expands back into tokens only when needed

↳ Keeps normal model decoding intact

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⚡ Performance gains

↳ 30.8× faster time-to-first-token

↳ 16× longer context with no accuracy loss

↳ 3.75× faster than prior SOTA methods

↳ Accuracy holds across RAG, summarization & chat

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💡 Why it matters

↳ Faster responses → smoother user experience

↳ Smaller memory use → reduced infra costs

↳ Cached embeddings → simpler deployments

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♻️ Restack to help someone learn AI the right way