Do you need an LLM with expertise on your domain and your proprietary data? Have prompting and RAG failed to deliver the accuracy improvements you need? In this guide, you'll learn about:

• Common methods for reducing LLM hallucinations—prompting, RAG, and fine-tuning—and how they compare
• Benefits of fine-tuning over other methods
• Methods that are best for specific use cases—text-to-SQL, code generation, factual reasoning, and text classification and summariztion
• How Lamini Memory Tuning works
• How to memory tune your first model

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“Lamini is magic! The fine-tuning was very fast. It lowers the barrier of fine-tuning for us developers who are not ML experts. I achieved 95.83% percentage of correct answers on my third iteration, while the RAG application only got 87.50%."
- Allan Ray Jasa, Lamini On-Demand user

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