Imagine hiring a brilliant assistant, handing them a 200-page manual, and asking a question. They read all 200 pages, answer, and then promptly forget everything. Next question? They read all 200 pages again. And you pay them by the page. That’s roughly how large language models (LLMs) work by default. Prompt caching is the fix. […]
Read More →Your agentic app just ran a search. The tool returned 500 results as JSON. Your agent appended all of it and fired off an API call — 45,000 tokens to answer a question that needed maybe 4,500. Tejas Manohar, a senior engineer at Netflix, hit this problem every day. He was running out of tokens […]
Read More →A plain-English reference guide covering the jargon that shows up every time a new language model drops, from parameter counts to quantization methods. Contents 01 · Architecture & Model Design — Transformer · Dense Model · Mixture of Experts · Active Parameters · Feed-Forward Network · Layers · Hidden Dimension · Attention Heads 02 · Attention Mechanisms — Multi-Head Attention · Multi-Query Attention · Grouped-Query Attention · KV Cache · Sliding Window Attention · RoPE · RoPE Theta 03 · Sizing, Scale & Counting — Parameters · Embedding Parameters · Non-Embedding […]
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