Prompt Caching in LLMs: Cut the Cost and Latency of Your AI Application

Learn how prompt caching works in the Anthropic, OpenAI, and Gemini APIs: what the cacheable prefix is, how to order your prompt to maximize hits, breakpoints with cache_control, TTLs and pricing, and the mistakes that silently invalidate the cache. With production-ready Python code and metrics to measure your hit rate. Extended edition: the KV cache under the hood, Anthropic automatic caching and the 1-hour TTL, OpenAI prompt_cache_key and 24h retention, Gemini implicit and explicit caching, the math of the savings, multi-tenant and cache-aware routing, RAG, agents and compaction, self-hosted with vLLM and SGLang, security, observability and alerts, a CI smoke test, a migration guide, anti-patterns, a full case study, and an FAQ.

36 minutes read

2026-07-28

0 views

Verificando acceso...

Loading comments...

Related Resources

Tutorial

Tutorial: Introduction to LangChain

Learn the basics of LangChain to build AI applications. From installation to your first functional pattern.

Guía

PREMIUM

Context Engineering for AI Agents: Compaction, External Memory, and Subagents

Learn to treat your agent's context window as the finite resource it is: the four levers of context engineering (write, select, compress, isolate), compaction with structured summaries, persistent memory outside the context, just-in-time retrieval, subagents with isolated windows, and tool-result hygiene. With production-ready Python code and the mistakes that silently degrade your agent. New expansion: the context-failure taxonomy (poisoning, distraction, confusion, clash), reasoning budgets with interleaved extended thinking, the real cost of multimodal context, managed memory (Letta, Zep, mem0), effective context length per RULER, and multi-agent handoffs with structured payloads. Latest expansion: the positional anatomy of context (lost in the middle and cache-aware placement), isolating untrusted content by design with Dual-LLM and CaMeL, and generating outputs longer than the window with outlines, rolling summaries, and patch-based revision. August extension: per-section token budgets with a degradation ladder, prompt compression with LLMLingua, semantic retrieval deduplication, self-hosted KV cache (vLLM and SGLang) with cache-aware routing, and memory evaluation with LongMemEval.

Guía

Guide: RAG in Production — Chunking, Embeddings, Hybrid Search, and Reranking

Battle-tested patterns for building production-ready RAG (Retrieval-Augmented Generation) systems in 2026: semantic chunking, embedding selection, hybrid search, and cross-encoder reranking.