> For the complete documentation index, see [llms.txt](https://learn.getvectorai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://learn.getvectorai.com/what-are-vectors/untitled/limitations-of-vectors.md). # Limitations of vectors **Assumed Knowledge**: Vectors\ **Target Audience**: General audience\ **Reading Time:** 2 minutes Vectors are largely dependent on the models being used. There are 2 main difficulties associated with using vectors in production engineering systems: * The difficulty of obtaining the right type of vectors * The difficulty of productionisation of vectors Let us go over the difficulties of each. ### The Right Vectors Choosing the right vectors for your use case can be difficult. When choosing a model for vectors, you need to ask yourself the following: * Which out-of-the-box model works best for this use case? * Is an out-of-the-box model good enough? If not, how do I fine-tune my vectors for my particular use case? * What is the model trained on? * If the model's vectors do not perform well - is it because it was not trained on the right data or was the training method not suitable? #### Why do some models need to be fine-tuned and how does that help? Firstly - what is finetuning? In the context of vectors, fine-tuning refers to the process of altering your vectors by changing the weights in your model. You can change the weights in your model by training the model further on your specific dataset so that similarities between certain images and items can be improved. To better understand the importance of finetuning, let us consider an example scenario of how Image2Vec can fail in identifying the same faces and why we may need to finetune a new model to produce Face2Vec. Image2Vec has been trained to identify similar images and this can be based on color, the orientation of the image, features of the image. However, it may prioritise images of 2 different people if they are in the same orientation. As a result, to improve the similarity between the same person in spite of orientations - we can finetune the model the objective of having higher similarity between the same individual over the same orientation. ### Productionisation difficulty Putting vectors into production can be a difficult task. One has to build out an API for the index, the encoder, provide options for advanced search functions with vectors and then figure out ways to then store search. There is a lot of work that has been done simply to get it running but more work needs to be done to get it to a useful state for productionisation. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://learn.getvectorai.com/what-are-vectors/untitled/limitations-of-vectors.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.