The article compares the two approaches, highlighting that while rules-based NLP is more predictable and easier to debug, it lacks the fluency of data patterning methods. Generative AI, though more flexible and context-aware, can be unpredictable and prone to errors like AI hallucinations. The author suggests that a hybrid approach, combining the strengths of both methods, could offer the best of both worlds, providing fluency while maintaining predictability in critical applications.
Key takeaways:
- Legacy NLP relies on grammar rules to parse and understand sentences, making it predictable but less fluent.
- Modern NLP uses generative AI and large language models to identify patterns in human writing, resulting in more fluent and humanlike interactions.
- The rules-based approach is easier to debug and more predictable, while the data patterning approach is more flexible but can produce unpredictable results.
- A hybrid approach combining both methods can offer the benefits of fluency and predictability, but must be implemented carefully to avoid drawbacks.