In the last section of this guide, we explored the various challenges faced by pretrained LLMs, ranging from hallucinations and sensitivity to long-term context limitations. While fine-tuning has been a reliable approach for enhancing model performance in specific tasks, there are other solutions worth mentioning. In this section, we’ll examine a few interesting techniques that go beyond traditional fine-tuning, each bringing distinct advancements in accuracy, reasoning, and adaptability to tackle these persistent challenges.