RAG
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Java
LLM Fine-Tuning: SFT, LoRA, QLoRA, RAG and Prompting
Large language models can perform many tasks without any additional training. They can answer questions, summarize text, generate code, classify…
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Enterprise Java
Introduction to HyDE: Hypothetical Document Embeddings for RAG
Retrieval-Augmented Generation (RAG) has become one of the most common approaches for building AI applications that need to work with…
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Core Java
LangChain4j RAG in Java: Embedding, Chunking, and Retrieval Without the Python Envy
A practical pipeline for document ingestion, embedding, and retrieval, all on the JVM, with a Spring AI comparison for good…
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Software Development
RAG Beyond Context Limits
Large Language Models (LLMs) have significantly improved the way organizations build AI-powered applications. One of the most successful patterns is…
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Enterprise Java
RAG Architecture on the JVM: Building a Production-Ready Pipeline With LangChain4j
A practical walkthrough of embedding models, vector stores, retrieval strategies, prompt engineering, and evaluation ā without leaving the JVM. 1.…
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Software Development
Vector DatabasesĀ for AI
The invisible infrastructure powering semantic search, RAG, and recommendations ā and how to pick the right one without regretting it…
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