Posts tagged: ai-llm-research
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The process of reviewing a new open-weight model like an AI product engineer
It's not just about looking at benchmarks. Here is a practical checklist for reading an open-weight model: model card, license, architecture, context, inference, evaluation, and product fit.
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Reading new LLM papers: which trends matter for AI Engineers?
A pragmatic approach to reading LLM papers: ignore the hype, and look for trends that actually affect how we build AI products.
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Optimizing LLM reasoning is not just about training bigger models
How inference-time scaling, self-consistency, verifiers, and reasoning budgets improve LLM quality, and when not to use them due to high costs.
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Evaluating chatbot/AI workflow: benchmark, verifier, LLM judge and real-world test cases
A practical note on LLM evaluation: benchmarks are just the starting point, while a real product needs test cases, verifiers, human review and metrics tied to the workflow.
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