What is Benchmark?
A benchmark is a fixed test suite that scores models on the same tasks so results can be compared. What good suites measure, where they mislead, and how builders use them to pick models.
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Plain words first
Glossary.
Short definitions of the ideas this journal keeps returning to. 8 of 30 terms published; a new one lands every weekday.
What is Embeddings?
Embeddings turn text, images, or rows into lists of numbers that place similar meanings close together. How the vectors are made and where builders use them for search and grouping.
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What is Fine-tuning?
Fine-tuning keeps training a ready-made model on focused examples so it follows one task, style, or domain more reliably. What changes under the hood, what it costs, and when builders choose it over prompting alone.
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What is Inference?
Inference turns a trained model and a fresh prompt into an answer, running the weights forward instead of learning. How serving differs from training, and where builders spend their latency and cost budget.
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What is prompt injection?
Prompt injection hides hostile instructions inside data a language model reads, steering it to disobey its real task. How the trick works and how builders counter it.
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What is Quantization?
Quantization shrinks a model by storing its weights in fewer bits so it fits smaller GPUs and laptops. How lower precision trades a little accuracy for speed and where builders reach for it.
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What is RAG?
RAG grounds a language model in documents retrieved at query time instead of memory alone, cutting hallucinations on facts the model never learned. How the retrieve-then-write loop works and where builders reach for it.
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What is Temperature?
Temperature is the sampling dial that sets how random a model's next token feels. What low and high settings do, and where builders set it for factual versus creative work.
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