[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Rohan Paul [@rohanpaul_ai](/creator/twitter/rohanpaul_ai) on x 73.7K followers Created: 2025-07-18 19:50:45 UTC 🧵 3/n. The picture lays out a search pipeline that trims a huge document pool to a tiny list that an LLM can read. A sparse model and a dense embedding model each grab about 1000 likely matches from a corpus that holds 10M-100M records. Their two hit lists are blended, then a multi-vector model checks finer details and keeps the best XXX. A heavier cross-encoder reranker scores those XXX pairs in depth and sends only XX winners forward. This step-by-step filter saves compute and storage, yet still feeds the LLM documents picked with richer signals than a single wide scan could manage.  XXX engagements  **Related Topics** [lists](/topic/lists) [llm](/topic/llm) [lays](/topic/lays) [Post Link](https://x.com/rohanpaul_ai/status/1946296603624747042)
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
Rohan Paul @rohanpaul_ai on x 73.7K followers
Created: 2025-07-18 19:50:45 UTC
🧵 3/n. The picture lays out a search pipeline that trims a huge document pool to a tiny list that an LLM can read.
A sparse model and a dense embedding model each grab about 1000 likely matches from a corpus that holds 10M-100M records.
Their two hit lists are blended, then a multi-vector model checks finer details and keeps the best XXX.
A heavier cross-encoder reranker scores those XXX pairs in depth and sends only XX winners forward.
This step-by-step filter saves compute and storage, yet still feeds the LLM documents picked with richer signals than a single wide scan could manage.
XXX engagements
