# 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.

{% content-ref url="/pages/Cl7eO4olEFgJIfkXis94" %}
[Prompt Engineering](/llm-guide/solutions-to-llm-challenges/prompt-engineering.md)
{% endcontent-ref %}

{% content-ref url="/pages/9SW3CMAtbwKOcWGqxXzV" %}
[Neuro-Symbolic Methods](/llm-guide/solutions-to-llm-challenges/neuro-symbolic-methods.md)
{% endcontent-ref %}

{% content-ref url="/pages/4reGC2yaeYH65YF0zwwF" %}
[Retrieval-Augmented Generation (RAG)](/llm-guide/solutions-to-llm-challenges/retrieval-augmented-generation-rag.md)
{% endcontent-ref %}

{% content-ref url="/pages/MRrVlCNyt4KYygSeeBdx" %}
[Honorable Mentions](/llm-guide/solutions-to-llm-challenges/honorable-mentions.md)
{% endcontent-ref %}


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