Unlike older methods that use an "unknown" placeholder, modern tokenizers built on Byte-Pair Encoding (BPE) can represent any string. When faced with a new word, they don't crash or lose information. Instead, they break it down into the smallest known components, ensuring universal representability.

For most enterprise tasks, massive frontier models are overkill—a "bazooka to kill a fly." Smaller, domain-specific models are often more accurate for targeted use cases, significantly cheaper to run, and more secure. They focus on being the "best-in-class employee" for a specific task, not a generalist.

Despite the hype, Datycs' CEO finds that even fine-tuned healthcare LLMs struggle with the real-world complexity and messiness of clinical notes. This reality check highlights the ongoing need for specialized NLP and domain-specific tools to achieve accuracy in healthcare.

A unified tokenizer, while efficient, may not be optimal for both understanding and generation tasks. The ideal data representation for one task might differ from the other, potentially creating a performance bottleneck that specialized models would avoid.

Beyond the obvious lack of non-English training data, Large Language Models are architecturally biased. Their tokenization process, designed for English, inefficiently breaks down other languages into more fragments. This increases operational costs and reduces comprehension, creating a structural disadvantage.

Generic AI documentation tools, often trained on primary care conversations in quiet rooms, fail in specialized fields. Physical therapy occurs in noisy, dynamic environments with unique terminology. TheraNow's success came from building its AI on a specific dataset of PT-patient interactions, tailored to that workflow.

A major misconception is that general-purpose Large Language Models (LLMs) can be readily applied to complex biological problems. Biological data, like RNA sequencing, constitutes a unique language that requires custom-built foundation models, not simply fine-tuning of existing LLMs.

An emerging rule from enterprise deployments is to use small, fine-tuned models for well-defined, domain-specific tasks where they excel. Large models should be reserved for generic, open-ended applications with unknown query types where their broad knowledge base is necessary. This hybrid approach optimizes performance and cost.

Custom tokenizers and embeddings, created for a foundation model, can be repurposed to enhance other data engineering tasks. They can improve OCR accuracy on domain-specific documents, allowing for better text-based processing and avoiding the higher cost of vision models.