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For Introduction, I am a Full Stack Web Developer + Gen AI and Open Source Contributor.
#RAG doesn’t always need embeddings.
Thanks to @piyushgarg_dev sir for introducing #Vectorless RAG with #PageIndexing can work better for long documents:
-> Follows hierarchy, not similarity
-> Improves traceability
-> Reduces irrelevant context
More:
https://t.co/4WXlB5vACA
What if your RAG system isn’t failing… it’s just retrieving the wrong context?
Common RAG failures:
→Irrelevant retrieval
→Poor chunking
→Weak ranking
→Context overload
Result? Hallucinations creep back in.
Fixing RAG = fixing retrieval first.
More 👇
https://t.co/EuDrZqWLm8
What if scaling AI wasn’t about bigger models… but smarter retrieval?
Advanced RAG =
→ Better ranking & re-ranking
→ Multi-step retrieval pipelines
→ Lower latency, higher reliability
It’s how you take #RAG from demo → production.
More 👇
https://t.co/ouVJEM5kJz
#AI#GenAI
@YashHustle_22 I would build the frontend first because it would give us the idea about what all features we are dealing with in the backend and how should we provide data in the frontend.
What if #AI could answer with real-time knowledge instead of guessing?
#RAG (Retrieval-Augmented Generation) =
>Fetches relevant data before generating
>Reduces hallucinations
>Improves accuracy + context
It turns static models into dynamic systems.
More:
https://t.co/PbEjry0Xlf