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how Gen.AI companies combine narrow ML components behind conversational interfaces to simulate intelligence. Each agent component (text generation, context management, tool integration) has direct non-ML equivalents. API access bypasses the deceptive UI layer, providing better determinism and utility. Optimal usage requires abandoning open-ended interactions for narrow, targeted prompting focused on pattern recognition tasks where these systems actually deliver value.
Learn end-to-end ML engineering from industry veterans at PAIML.COM
By Noah Gift5
44 ratings
how Gen.AI companies combine narrow ML components behind conversational interfaces to simulate intelligence. Each agent component (text generation, context management, tool integration) has direct non-ML equivalents. API access bypasses the deceptive UI layer, providing better determinism and utility. Optimal usage requires abandoning open-ended interactions for narrow, targeted prompting focused on pattern recognition tasks where these systems actually deliver value.
Learn end-to-end ML engineering from industry veterans at PAIML.COM

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