Full fine-tuning vs LoRA vs RAG - when to actually use each

All three augment a model's knowledge with new data, but they solve different problems.

→ Full-model fine-tuning — adjusts every weight on task-specific data. Works well, but impractical at LLM scale due to size, training cost, and the cost of maintaining each fine-tuned copy.

→ LoRA fine-tuning — decomposes weight matrices into low-rank matrices, trains only those, freezes the rest. Same idea as full fine-tuning, a fraction of the compute.

→ RAG — no further training at all. Embed your data once, embed the query at runtime, retrieve nearest neighbors, pass both to the LLM.

RAG isn't free of problems though.

Queries and answers are structurally different, so similarity matching often pulls irrelevant chunks.

And RAG can't summarize across a full dataset. The LLM only ever sees the top retrieved matches, never everything you've stored.

#RAG #LoRA #LLMFineTuning #MachineLearning #LLMEngineering

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