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Not everything needs an LLM: A framework for evaluating when AI makes sense
The answer to 'What customer needs requires an AI solution?' isn’t always 'Yes.' LLMs are still expensive and not always accurate.
If there are patterns to the combinations of inputs and outputs (like reviewing customer anecdotes to derive a sentiment score), consider supervised or semi-supervised ML models over LLMs because they might be more cost-effective. I put together a quick table below, summarizing the considerations above, to help project managers evaluate their customer needs and determine whether an ML implementation seems like the right path forward. Evaluate your customer’s need using the matrix above, taking into account the costs of implementation and the precision of the output, to build accurate, cost-effective products at scale.
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