๐ ๐ฅ๐ฒ๐ฏ๐๐ถ๐น๐ ๐ ๐ ๐ฅ๐๐ ๐ฃ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ ๐๐ฟ๐ผ๐บ ๐ฆ๐ฐ๐ฟ๐ฎ๐๐ฐ๐ต
I rebuilt my RAG pipeline. It was not because I needed a bigger model or better embeddings. It was because my system felt generic.
My first version followed the standard path:
- Embed the query
- Retrieve top chunks
- Pass them to the model
- Hope for the best
This works for simple Q&A. It fails for complex tasks like debate learning. In a debate, you need different types of evidence. You need definitions for background. You need clash material for arguments. You need vocabulary for language support.
A simple chunk search cannot tell the difference between these needs. It just finds text.
I stopped thinking of retrieval as finding text. I started thinking of it as making decisions about evidence. I moved from a simple search to a layered architecture.
Here is the new flow: Topic $\rightarrow$ Plan $\rightarrow$ Route $\rightarrow$ Preselect $\rightarrow$ Retrieve $\rightarrow$ Rerank $\rightarrow$ Pack $\rightarrow$ Teach $\rightarrow$ Evaluate
The real improvements came from these steps:
โข Query Planning: The system expands a topic into structured intent. Instead of just searching "feminism," it creates subqueries and specific search terms.
โข Intent Routing: The system decides what kind of evidence is needed. It routes the request to specific paths for definitions, examples, or coaching notes.
โข Document Preselection: The system picks the best documents first. Then it searches for chunks inside those specific documents. This is faster and more accurate.
โข Context Packing: I stopped dumping all text into one big block. I now separate evidence into lanes like "Definitions," "Mechanisms," and "Examples." This helps the model reason better.
โข Memory and Evaluation: The system remembers what worked. It uses real traces to measure if the retrieval plan makes sense.
The lesson is simple. Advanced RAG is not about adding more model calls. It is about making retrieval more deliberate.
If your RAG pipeline feels generic, do not blame your model. Look at your architecture.
Stop at chunk search is just the beginning. Start thinking about intent, routing, and evidence roles. That is how you build a system people can trust.
Optional learning community: https://t.me/GyaanSetuAi