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Giving your chatbot memory and personality without prompt bloat
A common first attempt at building a useful chatbot is to place everything inside the system prompt: the assistant’s personality, company rules, user preferences, previous conversations, examples, and
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Build your first AI chatbot: from prompt to a working assistant
A useful AI chatbot is not just “a prompt connected to a model.” Even the smallest real application needs a few moving parts: instructions that define behavior, user input, an API call, conversation s
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Model monitoring in production: drift, decay, and alerting
A conventional service usually fails loudly. It times out, returns an error, exhausts memory, or stops responding.
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Feature stores and reproducible training pipelines
A model is not reproducible merely because its training script has a fixed random seed. You must also reproduce the exact feature definitions, historical values, timestamps, dependency graph, and mode
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Data leakage: the silent killer of ML projects
Machine learning projects rarely fail because the algorithm is not advanced enough. More often, they fail because the model learned from information it would never have at prediction time.
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The bias-variance tradeoff, finally explained with runnable plots
Machine learning models rarely fail because they are too simple or too complex by accident. More often, they fail because they learn the wrong amount of information from data.