AmirhosseinHonardoust/RAG-vs-Fine-Tuning
A comprehensive, professional guide explaining the differences, strengths, and best practices of Retrieval-Augmented Generation (RAG) and Fine-Tuning for LLMs, including workflows, comparisons, decision frameworks, and real-world hybrid AI use cases.
Implements interactive workflows with vector database integration (FAIX, Pinecone, Chroma) and decision trees for choosing between approaches, plus practical code examples using Hugging Face transformers and LoRA optimization. Covers the hybrid pattern where RAG handles dynamic knowledge retrieval while fine-tuning enforces consistent tone and reasoning—a pattern used in enterprise chatbots and AI copilots.
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Oct 31, 2025
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