Why Language Models Became Large Language Models And The Hurdles In Developing LLM-based Applications

LLMs present an exciting new frontier of innovation. However, implementing these models into practical applications presents challenges related to their size and computational requirements. Efficiently managing the trade-offs between model capabilities and deployment scale is a crucial factor in overcoming these obstacles. Techniques like pruning, knowledge distillation, and vector databases can help optimize LLM integration. AssemblyAI's LeMUR framework simplifies this process by integrating LLMs within the entire AI stack for spoken data. It combines techniques such as prompt augmentation, retrieval methods, and structured outputs to handle audio data efficiently. Ongoing research continues to provide solutions that make deploying LLMs more feasible and effective.

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