![](https://crypto4nerd.com/wp-content/uploads/2023/07/1DWG9lD63cyjau-84S0gtBg-1024x386.png)
So for the same prompt, we can clearly see the difference between a vanilla completion provided by OpenAI ChatGPT and an enhanced version using embeddings and Vector Store.
This is just the tip of the iceberg, for a more powerful solution, there are Vector Databases e.g.: Weaviate or Pinecone, which can be consumed as a fully-managed service.
Alternatives
Last but not least an alternative to Vector Database is to fine-tune the model, by providing a set of training examples that each consist of a single input (“prompt”) and its associated output (“completion”).
One important note, fine-tuning is not about teaching the model new information is about transfer learning in which the model learns new tasks, and by task, I mean reusing information that already has. Finetuning is more difficult than prompt engineering.