m 17, self taught, dropped out of highschool, been deep in retrieval systems for a while now.
Started where everyone starts. LangChain, vector DBs, chunk-embed-retrieve. It works. But something always felt off. We’re treating documents like corpses to be dissected rather than hmm I dont know, something more coherent.
So I went back to first principles. What if chunking isnt about size limits? What if the same content wants to be expressed multiple ways depending on whos asking? What if relationships between chunks aren’t something you calculate?
Some observations from building this out:
On chunking. Fixed-size chunking is violence against information. Semantic chunking is better but still misses something. What if the same logical unit had multiple expressions, one dense, one…
m 17, self taught, dropped out of highschool, been deep in retrieval systems for a while now.
Started where everyone starts. LangChain, vector DBs, chunk-embed-retrieve. It works. But something always felt off. We’re treating documents like corpses to be dissected rather than hmm I dont know, something more coherent.
So I went back to first principles. What if chunking isnt about size limits? What if the same content wants to be expressed multiple ways depending on whos asking? What if relationships between chunks aren’t something you calculate?
Some observations from building this out:
On chunking. Fixed-size chunking is violence against information. Semantic chunking is better but still misses something. What if the same logical unit had multiple expressions, one dense, one contextual, one hierarchical? Same knowledge, different access patterns.
On retrieval. Vector similarity is asking what looks like this? But thats not how understanding works. Sometimes you need the thing that completes this. The thing that contradicts this. The thing that comes before this makes sense. Cosine similarity cant express that.
On relationships. Everyone’s doing post-retrieval reranking. But what if chunks knew their relationships at index time? Not through expensive pairwise computation, that’s O(n²) and dies at scale. Theres ways to make it more ideal you could say.
On efficiency. We reach for embeddings like its the only tool. Theres signal we’re stepping over to get there.
Built something based on these ideas. Still testing. Results are strange, retrieval paths that make sense in ways I didnt explicitly program. Documents connecting through concepts I didnt extract.
Not sharing code yet. Still figuring out what I actually built. But curious if anyone else has gone down similar paths. The standard RAG stack feels like we collectively stopped thinking too early.