DeeperMind.ai

Finally cracked large-scale semantic chunking — and the answer precision is 🔥

Hey 👋

I’ve been heads down for the past several days, obsessively refining how my system handles semantic chunking at scale — and I think I’ve finally reached something solid.

This isn’t just about processing big documents anymore. It’s about making sure that the answers you get are laser-precise, even when dealing with massive unstructured data.

Here’s what I’ve achieved so far:

Clean and context-aware chunking that scales to large volumes

Smart overlap and semantic segmentation to preserve meaning

Ultra-relevant chunk retrieval in real-time

Dramatically improved answer precision — not just “good enough,” but actually impressive

It took a lot of tweaking, testing, and learning from failures. But right now, the combination of my chunking logic + OpenAI embeddings + ElasticSearch backend is producing results I’m genuinely proud of.

If you’re building anything involving RAG, long-form context, or smart search — I’d love to hear how you're tackling similar problems.

Let’s connect and compare strategies!