Generative modeling

As explained in Spring AI in Action

Generative AI is artificial intelligence that produces new content such as text, images, audio, or code from learned patterns.

Generative artificial intelligence produces text, images, and other content from natural-language prompts. It enables applications to answer questions, follow instructions, summarize documents, classify or moderate content, and generate images. section 1.0 section 1.1

Spring AI connects Spring Boot applications to AI providers and language models through a consistent client abstraction. It handles prompt transmission and response processing instead of requiring application code to manage provider-specific HTTP details. section 1.4 section 1.1

A question-and-answer endpoint

A REST application can accept a question at /ask and return a generated answer. Repeating the same request may produce different responses because generative AI is probabilistic and nondeterministic. section 1.1.4

Figure 1.1 Spring AI coordinates interaction with AI providers and models.

Use the architecture figure to see how Spring AI coordinates application requests with AI providers and models. section 1.1

How Spring AI in Action covers it

How deep it goes

Defines generated content, prompts, and nondeterminism, then demonstrates a simple REST application without detailing model internals.

Examples

Uses question answering, document summarization, sentiment analysis, content moderation, classification, image generation, and sky-color responses.

What it assumes

Assumes familiarity with Spring Boot application development while introducing generative AI interaction through a client abstraction.

What you can do after

Build it from scratch as a Spring Boot question-and-answer service using Spring AI and a supported AI provider.

How it connects

Connects natural-language prompts, large language models, Spring Boot, REST services, and AI service providers.

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Related concepts

• language modelincludes · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• Gaussian mixture modelincludes · A Gaussian mixture model is a probabilistic model representing a distribution as a weighted combination of multiple Gaussian component distributions.
• socio-technical systempart of · A socio-technical system is an interdependent arrangement of people, technology, processes, and social or organizational structures that interact to achieve outcomes.
• artificial intelligencekind of · Artificial intelligence is the field concerned with creating computational systems that perform tasks associated with human intelligence, such as learning, reasoning, perception, or decision-making.
• variational autoencoderincludes · A variational autoencoder is a generative neural network that learns a probabilistic latent representation of data and decodes samples from it to reconstruct or generate data.
• generative adversarial networkincludes · A generative adversarial network is a generative model with a generator and discriminator trained in competition, enabling the generator to produce samples resembling a target dataset or distribution.

This explainer was generated by Manning's knowledge graph from the text of Spring AI in Action. The section links point to the parts of the book it draws on.