Topic
Prompt Caching
7 posts on prompt caching — part of glossary on the n4n AI blog.
What is prompt caching in LLM APIs?
Prompt caching lets LLM APIs reuse computed attention for repeated prompt prefixes, cutting latency and cost on long-context workloads.
Prompt caching vs KV cache: what's the difference?
Understand the technical differences between prompt caching and KV cache, when each applies, and how they affect latency, cost, and throughput in LLM inference.
Prompt caching pricing: how discounts actually work
How prompt caching pricing discounts work across major LLM providers, with concrete examples of cache hit mechanics, token accounting, and cost optimization strategies.
How to structure prompts for better cache reuse
Learn to structure prompts so prefix caching works — cut latency and cost by making repeated context cacheable across requests.
How prompt caching speeds up multi-turn conversations
How prompt caching reduces latency and cost in multi-turn LLM conversations, with concrete implementation patterns and honest tradeoffs.
How OpenAI's automatic prompt caching works
How OpenAI's automatic prompt caching reduces latency and cost by reusing prefix tokens across requests, with technical details on cache keys, TTL, and billing.
How Claude's prompt caching reduces API costs
Learn how to enable Claude prompt caching, structure requests for cache hits, and measure the cost reduction in your LLM API spend.
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Fine-Tuning Fundamentals9
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88