# Fine tuning vs. Embedding

**URL:** <https://community.openai.com/t/fine-tuning-vs-embedding/35813>\
**Category:** API\
**Created:** [January 14, 2023, 3:16am UTC](https://community.openai.com/t/fine-tuning-vs-embedding/35813 "2023-01-14T03:16:16Z")\
**Posts on this page:** 1\
**Showing post:** 10

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**Author:** ![giardina.v](https://sea2.discourse-cdn.com/openai1/user_avatar/community.openai.com/giardina.v/32/60932_2.png) [@giardina.v](https://community.openai.com/u/giardina.v)\
**Post date:** [March 31, 2023, 2:55pm UTC](https://community.openai.com/t/fine-tuning-vs-embedding/35813/10 "2023-03-31T14:55:19Z")

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I found this video quite useful in understanding the difference

[![](https://us1.discourse-cdn.com/openai1/original/3X/9/2/9275630ee6d6079a848e0942e61dbeedb04262b0.jpeg "What is Fine-tuning and Embeddings in GPT-3?") ](https://www.youtube.com/watch?v=_wtfszFzqt0)

From my understanding  
Fine-tuning is a way to add new knowledge to an existing model. So it’s a simple upgrade, same usage.  
Embedding is a way to let the model search in a “database” and return the best result. So it’s good for finding something specific for example.

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