Prompt Caching and Reuse: Design Prompts That Save 90% on Token Costs at Scale
The architecture decisions that turn a $500/day LLM bill into $50 without sacrificing output quality
Pillar
Chain-of-thought, few-shot, role prompting, structured output
43 articles • Page 1 of 5
The architecture decisions that turn a $500/day LLM bill into $50 without sacrificing output quality
The delimiter strategy that Claude, GPT, and Gemini all respond to better than plain English sections
Stop bolting retrieval onto bad prompts -- design prompts that actually use retrieved context correctly
OpenAI's own tests show trimming duplicated instructions raises scores 10-15% while cutting tokens up to 66%. Your old scaffolding is now a tax.
Seventeen models, thirteen spatial benchmarks, one verdict: "think step by step" makes maps, mazes, and layouts worse. Here's what to send instead.
Meta's semi-formal reasoning template forces premises, traced paths, and a derived conclusion -- so you can see exactly where the model is bluffing.
You can't command next-token prediction into honesty, but you can engineer the conditions where it stops guessing.
Diffusion models like Inception's new Mercury 2 refine the whole answer in parallel, so scaffold the output shape instead of nudging a linear reasoning chain.
Google's migration guide quietly killed the majority-vote trick you've leaned on for years. Here's what replaces it.