As AI continues to evolve, developers are increasingly tasked with integrating language models into their applications.
Frameworks like Spring AI and LangChain4j make this process easier, but to use them effectively, it’s important to understand the underlying concepts.
Those concepts are backbone of building robust AI-powered applications.
1.Models
At the core of AI integration are models—pre-trained large language models (LLMs) like GPT, LLaMA, or Mistral.
Spring AI and LangChain4j abstract away much of the complexity, allowing you to switch between models or providers with minimal code changes.
- Spring AI: Provides annotations and abstractions for working with multiple providers.
- LangChain4j: Offers modular APIs to plug in different model backends.
Treat models as pluggable components in your architecture, like an engine to a vehicle.
2.Prompts
Prompts are the instructions you send to models.
Prompt engineering is an art—how you phrase input significantly impacts output quality.
- Spring AI: Lets you define prompts using annotations and templates.
- LangChain4j: Supports dynamic prompt construction and chaining.
A prompt for summarization vs one for code generation will require very different structures.
3.Prompt Templates
A prompt template allows you to define reusable prompt patterns with variables.
Instead of hardcoding every instruction, you parameterize it.
- Spring AI: Supports templated prompts using placeholders and annotations.
- LangChain4j: Provides PromptTemplate builders for dynamic generation.
Example :
PromptTemplate template = PromptTemplate.from("Translate this text to French: {input}");
String prompt = template.apply(Map.of("input", "Hello, how are you?"));
// prompt: "Translate this text to French: Hello, how are you?"
Prompt templates ensure consistency and reduce duplication across use cases.
4.Tokens
LLMs don’t process words directly, they work with tokens (chunks of text).
Each model has a maximum token limit.
On input, models convert words to tokens.
On output, they convert tokens back to words.
- Important for budgeting API costs.
- Impacts prompt and context size.
Always keep an eye on token usage when building production apps.
Because Tokens = Money.
5.Structured Output
While models typically generate free-form text, sometimes we need predictable formats like JSON or domain objects.
Structured output enforces schemas so that responses are machine-readable.
- Spring AI: Supports JSON schema validation and mapping to DTOs.
- LangChain4j: Allows specifying expected output formats.
Example: Extracting a product catalog into structured JSON instead of raw response.
6.Embeddings
Embeddings are numerical representations of text, images, or videos that capture relationships between inputs.
They work by converting text, image, and video into arrays of floating point numbers, called vectors.
These vectors are designed to capture the meaning of the text, images, and videos.
The length of the embedding array is called the vector’s dimensionality.
By calculating the numerical distance between the vector representations of two pieces of text, an application can determine the similarity between the objects used to generate the embedding vectors.
They enable and power many retrieval-based workflows.
- Use cases: search, recommendation, clustering.
- Spring AI and LangChain4j provide APIs to generate embeddings from LLMs.
Think of embeddings as the bridge between raw text and machine-friendly vector space.
As a Java developer exploring AI, it’s not necessary to comprehend the intricate mathematical theories or the specific implementations behind these vector representations.
A basic understanding of their role and function within AI systems suffices.
7.Vector Databases
A vector database (like Pinecone, Weaviate, or PostgreSQL with pgvector) is optimized for storing and searching embeddings.
- Core to RAG workflows.
- Enables semantic search at scale.
Spring AI and LangChain4j integrate with vector databases for fast retrieval.
8.Chat Memory
Conversation-driven apps need chat memory to preserve context across turns.
- Short-term memory: Keeps track of the last few exchanges.
- Long-term memory: Stores embeddings for recall across sessions.
This is what makes chatbots feel more “human.”
9.Retrieval-Augmented Generation (RAG)
RAG is a technique that combines external knowledge with model responses:
- Retrieve relevant documents from a database (often using embeddings).
- Inject them into the prompt.
- Generate a response.
Both Spring AI and LangChain4j have patterns for building RAG pipelines.
10.Tool Calling
Models can be extended with tool calling (or function calling).
Instead of just generating text, they can trigger external functions/APIs.
- Example: “What’s the weather in Paris?” → Model calls a weather API → Returns formatted response.
This bridges natural language with real-world capabilities.
11.MCP (Model Context Protocol)
MCP is an emerging standard for enabling consistent context exchange between clients and LLM servers.
It ensures that prompts, tool calls, and responses remain structured and interoperable.
Think of MCP as the HTTP of LLM-powered systems.
12.Observability
With AI in production, observability is critical:
- Log prompts, responses, and token usage.
- Trace tool calls and database queries.
- Monitor latency and error rates.
Spring AI integrates with Spring Boot observability tooling, while LangChain4j offers hooks for logging and monitoring.
13.Evaluating AI Responses
Unlike deterministic systems, AI output can vary.
Evaluation is about measuring quality, relevance, and correctness.
- Human-in-the-loop feedback.
- Automatic metrics like BLEU, ROUGE, or embedding similarity.
Spring AI and LangChain4j support structured output to make evaluation easier.
Conclusion
As engineers, our role is not just to consume frameworks but to understand the why behind the abstractions.
Above concepts are the foundation for building reliable AI-powered systems.
By mastering these ideas and leveraging frameworks like Spring AI and LangChain4j, we can deliver applications that are both intelligent and production-ready.