"NLP is basically solved" --- somewhat but not the entirely yet. I work on a variety of specialized NLP use-cases for clients and there are different strengths

"NLP is basically solved" --- somewhat but not the entirely yet. I work on a variety of specialized NLP use-cases for clients and there are different strengths these approaches have. The big thing with LLMs is the ability to deploy something quickly if (a) if you can craft an appropriate prompt, (b) put up enough guardrails to stop surfacing hallucinated responses to users. For assistant or co-pilot kind of systems (b) is somewhat easy to deal with, since the user is expected to curate or edit the LLM's responses, hence their proliferation in such use-cases. Note though: if the LLM sounds authoritative enough it might bias the user into believing it is right - not a big problem when the user is relying on it for generating good prose, but this is a problem when presenting facts (esp. based on a specialized knowledge base). The downsides are (a) latency, (b) cost, and (c) the need for specialized training. In some applications, you require near real-time responses, and smaller models are still better here. The cost angle is a little tricky - it depends both on the volume of calls you want to make and if this cost translates into revenue for the possibly incremental benefit you derive from an LLM. As an example, lets say you have a chatbot that does NER/slot-filling using a spacy or stanza today, and lets also assume ChatGPT can do better - does the incremental accuracy, that comes at a cost since you're paying OpenAI - translate into incremental revenue (or profit)? I am not sure what the answer here is - its probably a NO right now, but there is a positive deferred benefit - as your chatbot solution becomes better in many small and large ways, it might sell better in the future. The specialized training part is when you can gather a use-case-specific dataset that can get a fairly good accuracy (comparable or greater than an LLM), esp. considering (a) and (b). Note that these concerns are largely true even for self-hosted LLMs like llama - just that the precise breakeven point changes.