> For the complete documentation index, see [llms.txt](https://ubiai.gitbook.io/llm-guide/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ubiai.gitbook.io/llm-guide/solutions-to-llm-challenges.md).

# Solutions to LLM Challenges

### **Beyond Fine-Tuning**

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.

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[Prompt Engineering](/llm-guide/solutions-to-llm-challenges/prompt-engineering.md)
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[Neuro-Symbolic Methods](/llm-guide/solutions-to-llm-challenges/neuro-symbolic-methods.md)
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[Retrieval-Augmented Generation (RAG)](/llm-guide/solutions-to-llm-challenges/retrieval-augmented-generation-rag.md)
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[Honorable Mentions](/llm-guide/solutions-to-llm-challenges/honorable-mentions.md)
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