{"id":14775,"date":"2023-08-30T08:30:45","date_gmt":"2023-08-30T15:30:45","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=14775"},"modified":"2025-06-13T22:42:09","modified_gmt":"2025-06-14T05:42:09","slug":"what-is-generative-ai","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/what-is-generative-ai\/","title":{"rendered":"How Generative AI Works with Couchbase"},"content":{"rendered":"

Welcome to the season of AI. Generative AI<\/a>. With this blog we will explain how generative AI works with Couchbase. We know generative AI is going to be a huge issue and opportunity for nearly every data-enabled enterprise application. In this blog, we\u2019ll dive into what generative AI is, how generative AI works, and where Couchbase fits in the world of generative AI.<\/span><\/p>\n

What is Generative AI?<\/h2>\n

Generative AI (Artificial Intelligence) is the categorical term used to describe content creation algorithms that use Large Language Models (LLMs) to construct new content based on similarity-based knowledge that the LLM has accumulated.\u00a0 The content generated can be text, code, graphics, pictures, audio, speech, music or video. The LLM is interrogated using prompts that suggest the topic and format of the desired output by a user or a program. The resulting content is generated and refined by the model, thus the term “Generative AI.”\u00a0<\/span><\/p>\n

How does Generative AI work?<\/h2>\n

Generative AI was born from advancements in natural language processing (NLP) and image processing, which helped identify the intended meaning of a phrase, and recognize objects like trees and rocks within an image. Each of these examples is built using algorithms that detect similarities. For example, an algorithm could identify that both tigers and tabby’s are similar types of cats. In many cases, these similarities are expressed along a large number of dimensions, and the algorithm will create numerical representations as vectors that describe how closely similar an item or phrase is to another. <\/span>See our blog on vector search.<\/span><\/a><\/p>\n

The challenge with these algorithms is that they must be trained to recognize when an item is similar to another, already recognized, item. This training requires feeding the models vast amounts of data allowing the model to compound and expand its field of recognition. Some LLMs have been trained by scanning the entire contents of the internet in order to build its knowledge base and understanding.<\/span><\/p>\n

Examples of Generative AI Tools<\/h2>\n

Key players in building and accessing generative AI models include<\/span> AWS<\/span><\/a>, <\/span>Open.AI<\/span><\/a>, <\/span>Microsoft<\/span><\/a>,<\/span> Google<\/span><\/a>, <\/span>Meta<\/span><\/a> and<\/span> Anthropic<\/span><\/a>. Each has released their own models in both free and enterprise versions. We believe that these vendors will create a centralizing gravitational force with their LLMs, similar to what they have done with their cloud and social services, as managing LLMs is incredibly resource-intensive.<\/span><\/p>\n

We are also seeing an emergence of thousands of generative AI browser plug-ins, as exemplified in this<\/span> ZDNet article.<\/span><\/a> These plug-ins are helping end users with content creation of all kinds. For Couchbase, we have announced our private preview of our own generative AI-powered coding assistant,<\/span> Couchbase iQ.<\/span><\/a><\/p>\n

Why Couchbase is a player in Generative AI-powered Applications<\/span><\/h2>\n

Couchbase is already being used as a data platform for <\/span>AI-powered applications<\/span><\/a>. There are a number of reasons for this, including the value and flexibility of the JSON data format, the versatility of data access patterns afforded by Couchbase, and the introduction of our newest capability, <\/span>Couchbase Capella iQ.<\/span><\/a><\/p>\n

JSON is an ideal data format for AI-oriented data<\/h3>\n

Prompt engineering is the process of building generative AI prompts that contain the proper amount of contextual data in order to receive a relevant and accurate response from an LLM. Prompt engineering is a fast-growing field of expertise and JSON is an ideal format for storing prompt data. Consider that:<\/span><\/p>\n