  The development of generative large language models (G-LLM) opened up new
opportunities for the development of new types of knowledge-based systems
similar to ChatGPT, Bing, or Gemini. Fine-tuning (FN) and Retrieval-Augmented
Generation (RAG) are the techniques that can be used to implement domain
adaptation for the development of G-LLM-based knowledge systems. In our study,
using ROUGE, BLEU, METEOR scores, and cosine similarity, we compare and examine
the performance of RAG and FN for the GPT-J-6B, OPT-6.7B, LlaMA, LlaMA-2
language models. Based on measurements shown on different datasets, we
demonstrate that RAG-based constructions are more efficient than models
produced with FN. We point out that connecting RAG and FN is not trivial,
because connecting FN models with RAG can cause a decrease in performance.
Furthermore, we outline a simple RAG-based architecture which, on average,
outperforms the FN models by 16% in terms of the ROGUE score, 15% in the case
of the BLEU score, and 53% based on the cosine similarity. This shows the
significant advantage of RAG over FN in terms of hallucination, which is not
offset by the fact that the average 8% better METEOR score of FN models
indicates greater creativity compared to RAG.
