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Core AI Workflows

16 essential AI design patterns — explained with clear diagrams, real Spring Boot microservice code, and real project scenarios. Understand how each pattern works, when to use it, and what to avoid.

16AI Patterns5Categories☕Spring Boot Code🎯Real Scenarios⚠️Common Pitfalls

16 workflows

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RAG — Retrieval-Augmented Generation

RetrievalIntermediateVector SearchContext InjectionGroundingpgvector

Fetches relevant documents from a knowledge base before generating an answer — grounding the LLM in real data instead of potentially hallucinated training memory.

Flow Diagram

User Query

→

Embed Query

→

Vector Search

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Inject Context

→

LLM Answer

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Grounded Output

■ Input■ Process■ Output

When a user asks a question, RAG converts it into an embedding vector and searches a vector database for the most semantically similar documents. Those documents are injected into the LLM prompt as context. The LLM then generates an answer grounded in that real, retrieved data — not its potentially outdated training memory.

This is the most important pattern for enterprise AI because it separates the knowledge store from the reasoning engine. You can update your documents without retraining the model. The LLM stays the same; your data changes freely.

Think of it as giving the LLM a "cheat sheet" before every answer. Without RAG, the LLM guesses from memory. With RAG, it reads the relevant facts first.