8 posts
The strongest levers to control LLM cost in production: token economics, prompt caching, semantic cache and model routing, illustrated with 2026 pricing moves.
With long context we can now put hundreds of examples in the prompt. A field guide to many-shot, trade-offs vs fine-tuning, prompt caching, and Turkish practices.
The three layers of cutting the LLM bill: model, system and application. Prompt caching, model routing, batching and Turkey-specific FX and KVKK risks.
Prompt engineering didn't die, it matured. Build a resilient prompt architecture with structured output, prompt caching, self-consistency, and programmatic tool calling — in Turkish and KVKK contexts.
What is LLM cost optimization? Techniques that cut token cost in production: prompt caching, batching, model routing, prompt trimming, RAG context reduction and FinOps discipline.
Prompt caching cuts input token cost up to 90% on Anthropic and 50% on OpenAI. I cover both approaches, when to pick which, and cache-hit design patterns.
I walk through how I cut a production LLM bill in half, sometimes to a fifth: prompt caching, model routing, self-hosted quantization and the observability that makes it all visible. With a Turkey and KVKK lens, concrete cost math and a tactics table.
Prompt engineering is dead, context engineering is alive. Anthropic's 90% cost-cutting prompt caching, GPT-5.5's 272K input threshold, Claude Opus 4.7's 1M context, and agent runtime state management are rewriting AI engineering in 2026. Turkish token efficiency, KVKK-compliant state stores, the 'Don't Break the Cache' principle.