Implemented Retrieval-Augmented Generation (RAG) to ground LLM responses in relevant enterprise knowledge.
Generated and managed text embeddings for semantic similarity and contextual information retrieval.
Integrated vector database/search capabilities for efficient retrieval of relevant documents and knowledge.
Designed AI orchestration workflows connecting LLMs, knowledge sources, REST APIs, business services, and external tools.
Implemented structured AI outputs to transform natural-language model responses into predictable application-level data.
Developed context-aware conversational flows for maintaining relevant information across interactions.
Designed the backend using Spring Boot and Spring AI, enabling integration with existing enterprise services and APIs.
Implemented prompt and context management to improve response relevance and consistency.
Designed the system with extensibility in mind, allowing additional knowledge sources, tools, and business capabilities to be integrated into the chatbot.
Case study
COMPLETEDA production-oriented conversational AI application developed using Spring AI and Large Language Models (LLMs) to provide context-aware interaction with enterprise knowledge and services. The application uses RAG, embeddings, vector search, and AI orchestration to retrieve relevant information and generate grounded responses based on available business context.
System / 01Healthcare systems
Java 17Spring BootSpring AICloud Large Language Models (LLMs)Retrieval-Augmented Generation (RAG)EmbeddingsVector DatabaseSpring SecurityREST APIsJPA/HibernatePostgreSQLDockerMaven