Let AI Be Your Docs

Embeddings are representations that allow computers to handle complex, multi-dimensional data efficiently. They convert text or other forms of data into numerical arrays, making it easier for AI models to process and understand the content semantically, rather than just syntactically.

Vector search enhances AI applications by allowing them to perform semantic searches, which focus on the meanings behind words rather than just the words themselves. This capability enables applications to return more relevant results by understanding the context and relationships between data points.

Yes, embeddings can be generated for various types of data including text, images, and audio. Different embedding models are optimized for different data types, enabling more accurate and relevant search results across a wide range of applications.

Costs associated with using AI models like GPT primarily arise from data storage and query processing. Storing embeddings and other data in databases incurs costs, and each query made to the AI models can result in charges, depending on the complexity and frequency of the queries.

Prompt engineering involves crafting inputs (or prompts) given to AI models to elicit specific types of responses or behaviors. Effective prompt engineering helps in fine-tuning the AI's outputs, ensuring that the responses are aligned with the desired outcomes or objectives.