How to Choose RAG vs Fine-Tuning (Without regret)
Should you pick fine tuning vs RAG, RAG vs fine tuning, when you want to train LLM on your data and ship custom AI with your data? In this video, I break down what is really happening under the hood, from LLM fine tuning with LLM weights, a loss function, a training loop, and supervised fine tuning, to retrieval augmented generation with prompt engineering, context injection, a vector database, embeddings, similarity search, semantic search, and vector search inside a practical RAG pipeline.
You will see real client outcomes for LLM customization, including fine tuning limitations, RAG limitations, troubleshooting RAG, and RAG best practices for a knowledge base chatbot, document retrieval, grounding LLM, and how to reduce hallucinations when you build custom chatbot systems, from a simple enterprise chatbot to more advanced setups.
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