Core AI Concepts Every Spring AI Developer Should Know

Models

AI models are algorithms designed to mimic human cognition, learning patterns from massive datasets to perform prediction, text, image, or other generation tasks.

Many model families exist; generative models like ChatGPT attract attention, but earlier text‑to‑image models such as Midjourney and Stable Diffusion already demonstrated diverse input‑output capabilities.

Spring AI supports language, image, and audio models for both input and output. Some models return numeric embeddings, a data structure that Spring AI can also handle.

Models such as GPT are pre‑trained, turning AI into a general‑purpose developer tool that does not require deep machine‑learning expertise.

Prompt (提示词)

Prompts are the language inputs that guide model behavior; they are more than simple text strings.

In the ChatGPT API, a prompt can contain multiple messages, each assigned a role (e.g., system role to set context, user role for direct input). Effective prompting is both an art and a science, giving rise to the discipline of Prompt Engineering.

Designing high‑quality prompts can markedly improve output quality, and the practice of sharing prompts has become a community activity backed by academic research.

Prompt Templates

Spring AI uses the open‑source String Template library to create dynamic prompts. A simple template might be: "Please tell me a {adjective} joke about {content}."

Developers provide a model object—typically a java.util.Map instance—to fill placeholders. Rendering the template produces the final prompt string.

Prompt formats have evolved from plain strings to complex multi‑message structures, each message representing a distinct role for the model.

Embeddings

Embeddings convert text, images, or video into numeric vectors that capture semantic relationships.

The vector length (dimension) determines how many floating‑point numbers represent the input. Similarity between two pieces of content is measured by the distance between their vectors.

For Java developers new to AI, a high‑level understanding of embeddings is sufficient for integrating AI features, especially in Retrieval‑Augmented Generation (RAG) scenarios.

Tokens

Tokens are the basic building blocks of model input and output; text is tokenized into a sequence, and generated token sequences are detokenized back into readable text.

In English, one token roughly corresponds to 75 % of a word.

Token usage directly impacts cost for hosted AI services, as pricing is usually based on the total number of input and output tokens.

Models also impose a context‑window limit (e.g., ChatGPT‑3: 4 K tokens; GPT‑4: 8 K, 16 K, or 32 K options), restricting the amount of text that can be processed in a single call.

Structured Output

Developers often need model responses in a specific format (e.g., JSON) rather than raw text.

Even when a model is instructed to reply in JSON, the result is a java.lang.String that may contain formatting errors.

Spring AI adds explicit support for structured output, allowing direct mapping of model replies to Java POJOs or other data structures, reducing parsing effort and improving reliability.

Bringing Your Data & APIs to the AI Model

To supply a model with up‑to‑date information, three approaches are discussed:

Fine‑tuning : retraining the model’s weights, which is resource‑intensive and often unavailable for large LLMs.

Prompt filling (RAG) : embedding your data into the prompt, a practical alternative supported by Spring AI.

Tool calling : registering custom services so the model can invoke external APIs for real‑time data.

RAG (Retrieval‑Augmented Generation)

RAG addresses the problem of incorporating relevant external data into prompts to improve response accuracy.

Tool Calling

After training, large language models are frozen and cannot access fresh data. Tool calling lets developers register methods (annotated with @Tool) that the model can invoke, connecting the LLM to external APIs for up‑to‑date information and data processing.

Spring AI abstracts the dialogue flow for tool calls, allowing multiple tools to be defined and referenced within a single prompt.

Summary & Outlook

Spring AI abstracts the complexity of AI, giving Java developers a powerful yet easy‑to‑use toolkit. By mastering models, prompts, prompt templates, embeddings, tokens, structured output, and integration techniques such as RAG and tool calling, developers can build smarter, more valuable applications without deep AI expertise.

As AI continues to evolve, Spring AI will keep advancing, offering stronger support for the Java ecosystem and helping both beginners and seasoned developers stay ahead in the AI era.