Traditional machine learning (ML) models have paved the way for groundbreaking advancements, but a new paradigm is emerging: domain-specific LLMs.
Preprocessing domain-specific documents for training an LLM comes with its own set of challenges. As one example, many times organizations will have to extract text and tables from PDFs, though many tools are available to help with this task like PyPDF2, pdftotext, pymupdf, pdfminer and Camelot.Once a domain-specific LLM has been adopted, organizations must train and fine-tune the LLM, which requires significant computational resources and time.
• Prompt engineering can also provide prompts that can be used to generate synthetic data, reducing the need for large, high-quality datasets. This can help reduce the amount of time and resources needed to train the model.assist developers in the prompt engineering process, particularly for LLMs. The platform was introduced by LangChain in 2023. The focus of LangSmith is to assist developers in closing the performance gap between prototype and production models.
Along with prompt engineering, techniques like PEFT can reduce resource requirements for fine-tuning. Likewise, opting for pre-trained models that are optimized for specific hardware configurations can also be advantageous.While the need for domain-specific LLMs is clear for organizations to work with industry-related data, the process of adopting these solutions can be daunting.
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