Prompt
Do I need to re-embed everything when I switch models?
Latest observation
Usually yes—if you switch to a different embedding model, you should re-embed your documents and queries with that same model.
Why:
- Embeddings from different models live in different vector spaces.
- A vector produced by Model A is generally not comparable to one produced by Model B.
- If you mix them, similarity search results can become unreliable or meaningless.
When you can avoid re-embedding:
- If you switch to a new version of the same embedding model that is explicitly backward-compatible.
- If the vectors are only being used internally for something unrelated to semantic comparison.
- If you keep the old index and only use the new model for new documents, but then you’ll have separate indexes or you’ll need a migration plan.
Best practice:
- Re-embed all stored content when changing embedding models.
- Also re-embed any cached query embeddings.
- Rebuild or re-index your vector database with the new vectors.
If you want, I can help you figure out whether your specific model change requires a full re-embed.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
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