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Calibration and Confidence in Probabilistic Outputs
Calibration and Confidence in Probabilistic Outputs Modern AI systems make predictions under uncertainty. That is true for a spam filter, a speech recognizer, and a language model answering a question. The difference is that language makes uncertainty harder to see. A model can produce a fluent sentence that reads like a fact even when the […]
Context Windows: Limits, Tradeoffs, and Failure Patterns
Context Windows: Limits, Tradeoffs, and Failure Patterns A context window is not memory. It is a temporary workspace. It holds the text and signals a model can attend to while generating the next token. This sounds simple, but it shapes almost every failure pattern users complain about: forgetting instructions, contradicting earlier statements, losing track of […]
Error Modes: Hallucination, Omission, Conflation, Fabrication
Error Modes: Hallucination, Omission, Conflation, Fabrication If you have ever deployed AI into a real workflow, you already know the uncomfortable truth: the hardest failures are not obvious crashes. The hardest failures are plausible outputs that are subtly wrong. In language systems, those failures often look like helpful explanations, confident summaries, or polished reports. People […]
Prompting Fundamentals: Instruction, Context, Constraints
Prompting Fundamentals: Instruction, Context, Constraints Prompting looks simple because it is written in natural language. That surface simplicity hides the fact that a prompt is an interface contract. It is a compact specification for what you want, what you consider acceptable, what information the model may use, and how the model should behave when it […]
Reasoning: Decomposition, Intermediate Steps, Verification
Reasoning: Decomposition, Intermediate Steps, Verification A model that speaks fluently can still be wrong. That sentence captures a core reality of modern AI: language is not the same as truth, and confidence is not the same as correctness. When people talk about “reasoning,” they often mean “the model gave an answer that felt like a […]
Tool Use vs Text-Only Answers: When Each Is Appropriate
Tool Use vs Text-Only Answers: When Each Is Appropriate A lot of AI disappointment comes from asking a text generator to behave like a system. A model can write, explain, summarize, and brainstorm with speed and style. But when you need correctness, freshness, traceability, or action, pure text is the wrong interface. Tool use is […]
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