What is Vector database?
A vector database stores embedding vectors so search finds meaning, not keywords. How similarity indexes work and how builders use them.
Ideas, collected
Essays, guides and observations. Find something worth sitting with.
A vector database stores embedding vectors so search finds meaning, not keywords. How similarity indexes work and how builders use them.
A system prompt is the hidden instruction block that sets a model's role, rules, and tone. What it controls and how builders shape it.
A context window is the stretch of tokens a model can read at once. What sets its size, what happens past the edge, and how builders fit work inside it.
Hallucination: a fluent model answer that sounds true but is not grounded in sources. Why models invent facts and how builders stop it.
Benchmark: a fixed test suite scoring models on the same tasks so results compare. What good suites measure and where they mislead.
Temperature is the sampling dial for token randomness. What low and high settings do, and where builders set it for fact vs flair.
Inference turns a trained model plus a fresh prompt into an answer. How serving differs from training, latency and cost budgets.
Quantization stores model weights in fewer bits to fit small GPUs and laptops. Accuracy-vs-speed tradeoffs and when builders use it.
Fine-tuning keeps training a ready model on focused examples for one task or style. What changes, what it costs, prompting vs tuning.
Embeddings turn text, images, or rows into number lists placing similar meanings together. How vectors are made and used for search.
RAG grounds a model in documents retrieved at query time instead of memory alone. How the retrieve-then-write loop works for builders.
Prompt injection hides hostile instructions in data a model reads, hijacking its task. How the trick works and builder defenses.
Why Machine Made Worlds exists: a manifesto for slow, useful writing about AI and automation in a noisy web.
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