Choosing an embedding model for RAG (and when to switch)

Pranjul Rathour
Pranjul Rathour

Originally published at https://pranjulrathour.scult.in/blog/embeddings-models-for-rag-how-to-choose. That copy is the canonical version and gets updates first.

The embedding model decides what "similar" means in your retriever. Swap it and the same query returns different chunks. Yet most projects pick one from a tutorial and never revisit it. Here is how I choose, and how I keep the option to change later.

What actually differs between models

  • Dimension — 384 versus 1024 dimensions changes index size and search cost more than it changes quality on small corpora.
  • Context length — a model that truncates at 512 tokens silently ignores the end of long chunks. Match chunk size to the model.
  • Domain — general web models handle policy PDFs well; code and legal text benefit from models trained on them.
  • Language — Hindi and Hinglish queries against English documents need a multilingual model, or you will watch retrieval fail on your own users.
  • Hosting — an API model is simplest; a local model keeps documents on your server, which some clients require.

Test before you commit

Take the evaluation set from how to evaluate a RAG system and index the corpus with two or three candidate models. Compare recall@5 and the rank of the correct passage. Differences are often smaller than tutorials suggest — and when they are small, pick the cheaper, faster or more private model.

Pranjul Rathour, GenAI engineer, Kanpur
Pranjul Rathour, GenAI engineer, Kanpur

Design for switching

Store the model name and dimension alongside each index, keep the embedding call behind one function, and make re-indexing a command rather than a migration. RAG.NextUpgrad's multi-provider fallback exists for generation, but the same discipline applies to embeddings: never let a single vendor's model become load-bearing for your architecture.

When to switch

Switch when recall on your own questions is the bottleneck and hybrid search plus reranking have already been added — not because a leaderboard changed. Leaderboards measure someone else's queries.

In a packed college auditorium
In a packed college auditorium

Pranjul Rathour · GenAI engineer, 3x hackathon winner, campus mentor. Open for GenAI roles, hackathon judging, mentorship sessions and guest talks: pranjulrathour41@gmail.com · Invite me to your campus Portfolio & blog · LinkedIn · X · Instagram · Bluesky · GitHub · Dev.to

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7 Levels of RAG Apps
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Pranjul Rathour
Pranjul Rathour
GenAI engineer, Kanpur · 3x first-prize hackathon winner · campus mentor
I ship production RAG pipelines, fine-tune LLMs and build agentic AI products end to end. I lead engineering at SCULT INDIA for a 14-member team and have mentored 200+ students through TechVerse Enclave.
Open to: GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges.
On stage, at hackathons and on campus
Pranjul Rathour
Pranjul Rathour
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On the mic
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Presenting to a room

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