When evaluating API-based versus self-hosted models, organizations should balance quick iteration with long-term costs and privacy concerns, as each option has distinct advantages.
Before opting for fine-tuning a model, practitioners should leverage prompt engineering in tandem with retrieval augmented generation (RAG), as they can enhance performance and effectiveness.
While smaller open models might not always rival the performance of larger closed models like GPT-4, they frequently suffice for numerous applications, and organizations should consider implementing them.
Hallucination poses risks with LLMs, yet employing retrieval augmented generation using credible sources can effectively diminish this issue, thereby increasing trustworthiness and accuracy.
To successfully integrate LLMs, companies must prioritize employee education, ensuring that teams understand both the potential benefits and limitations of these advanced models.
#large-language-models #api-vs-self-hosted #prompt-engineering #retrieval-augmented-generation #employee-education
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