LLMs Solved Language. That Was the Easy Part.
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A few years ago, if you wanted to build an intelligent chatbot, most of your effort went into getting the computer to understand people. Intent classification, entity extraction, stemming, confidence scores, dialogue trees, fallback logic. You annotated thousands of examples, hand-crafted regular expressions, and tuned thresholds until the system stopped misreading "cancel my order" as a request to place one. The hardest problem was not deciding what the system should do . It was figuring out…
1Key Takeaways
- A few years ago, if you wanted to build an intelligent chatbot, most of your effort went into getting the computer to understand people.
- Intent classification, entity extraction, stemming, confidence scores, dialogue trees, fallback logic.
- You annotated thousands of examples, hand-crafted regular expressions, and tuned thresholds until the system stopped misreading "cancel my order" as a request to place one.
- The hardest problem was not deciding what the system should do .
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — AI reports that a few years ago, if you wanted to build an intelligent chatbot, most of your effort went into getting the computer to understand people.
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