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Small language models: when a smaller model is enough
A smaller language model may be enough for a narrow task. The decision should come from evidence about that task.
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Multilingual AI: evaluate meaning, not just translation
Multilingual quality means preserving meaning in context, not merely producing fluent sentences.
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Fine-tuning: when model customization makes sense
Fine-tuning changes model behaviour through additional training. It works best when the target behaviour can be demonstrated and evaluated.
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Open weights: access, licensing and practical trade-offs
Open weights provide access to model parameters. Licensing, reproducibility and operating responsibility still need separate attention.
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Code-switching: testing AI across mixed-language conversations
Mixed-language conversations deserve their own evaluation because real users do not always stay within one language label.
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Prompting: write a useful brief before asking for output
A useful prompt resembles a good work brief: a clear task, relevant context and a definition of success.
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Local AI: what running a model on your computer means
Local AI runs model inference on hardware you control. It changes where computation happens, not every part of the surrounding data flow.
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AI for research: build a traceable reading workflow
AI research assistance is most useful when the reading trail remains visible.
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System instructions: setting the boundaries of an assistant
System instructions describe the assistant’s role and operating boundaries. They work alongside permissions and application logic.
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Quantization: shrinking models without ignoring quality
Quantization reduces the precision used to represent a model. The useful compromise depends on the task and hardware.
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AI for writing: separate factual review from style
Separate checking the facts from shaping the prose. The two passes solve different problems.
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Tool calling: how models connect to useful actions
Tool calling lets a model request a defined operation. The application remains responsible for executing and checking it.
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