Vector RAG finds similar text | | Vectorless RAG finds the RIGHT place.
Two completely different philosophies:
→ Traditional RAG: chunk, embed, search, hope
→ Vectorless RAG: index structure, route query, navigate, retrieve precisely
For long structured documents — legal, financial, technical — vectorless can be far superior.
No vector DB needed. No irrelevant matches. Just precise section retrieval.
The tradeoff? Your document structure needs to be solid.
Which approach are you using in your RAG pipelines? Drop it below
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1 - Indexing:
Check the query patterns of your application and create the right indexes.
2 - Materialized Views:
Pre-compute complex query results and store them for faster access.
3 - Denormalization:
Reduce complex joins to improve query performance.
4 - Vertical Scaling
Boost your database server by adding more CPU, RAM, or storage.
5 - Caching
Store frequently accessed data in a faster storage layer to reduce database load.
6 - Replication
Create replicas of your primary database on different servers for scaling the reads.
7 - Sharding
Split your database tables into smaller pieces and spread them across servers. Used for scaling the writes as well as the reads.
Over to you: What other strategies do you use for scaling your databases?
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