Optimizing LLM Performance with Caching | Generated by AI
Great, let’s dive deeper. You’re basically describing a prompt/result caching optimization problem in large language model (LLM) systems. Let’s break it down step by step:
1. Why caching matters for LLMs
- High cost per token: Running inference on big models (Claude Opus, GPT-4, etc.) is expensive because each token runs through billions of parameters.
- Redundant work: Many queries share large overlapping sections (e.g., system prompts, doc contexts, JSON wrappers). Without caching, the model recomputes them each time.
- Low-latency requirement: Products like copilots, chat assistants, and agents must respond quickly, so cache hits improve both speed and cost.
2. What gets cached?
There are a few layers:
- Prompt prefix caching (Key-Value Cache in Transformers)
- Inside the model: once tokens are processed, their hidden states (KV pairs) can be reused without recomputation if the same prefix reappears.
- Example: If 90% of your prompt is a fixed system context, and only the last 10% changes, you want to reuse that initial work.
- Response caching
- Outside the model, you can cache standard question → answer pairs (good for FAQs, but less flexible for dynamic contexts).
- Generally more useful for retrieval systems or simple API calls.
- Serialization & representation caching
- E.g., Manus’ optimization: by fixing JSON serialization order (
{"a":1,"b":2}vs{"b":2,"a":1}), repeated requests hash to the same cache key. - This prevents “accidental cache misses” caused by non-deterministic ordering of otherwise identical inputs.
- E.g., Manus’ optimization: by fixing JSON serialization order (
3. Prompt construction strategies
Your point is correct: put stable/static content first, dynamic variables last.
- Why? Model KV-cache works sequentially. If the first N tokens are identical, you can reuse that entire prefix. Any differences earlier in the prompt invalidate reuse.
- Example:
❌ Bad:"User: 12345\n[dynamic part]\nSystem rules...\n"
✅ Good:"[System rules...]\n[Context]\nUser: 12345"
→ With the good version, all the “rules/context” can be cached.
This applies at scale in copilots (like Cursor, GitHub Copilot, Replit) which serve millions of similar requests where only the user’s last few keystrokes differ.
4. Impact on different usage settings
- Individual users: Low usage → little benefit, since cache reuse across prompts is rare.
- Enterprise copilots / coding assistants / agents:
- Users often share 70–90% of the system + instruction prompt (only user query changes).
- At scale (thousands/millions of requests per day), cache savings are enormous.
- Flat-fee SaaS products:
- Providers bear the marginal cost, so caching is essential to remain profitable.
- Per-token billing (e.g., direct API):
- End-user pays anyway, but providers might still cache internally to reduce GPU load.
5. Additional caching/optimization strategies companies use
- Attention Key-Value (KV) Reuse: Persistent KV caches across turns in a conversation, instead of reprocessing past context each time.
- Chunked prompts: Split large contexts into frozen + dynamic chunks, reuse frozen.
- Deterministic hashing across inputs (normalize whitespace, sort JSON keys, remove irrelevant metadata).
- Speculative Decoding: Multiple models (a cheap small one guesses, big one verifies) – not cache, but another form of efficiency optimization.
- Vector DB + Retrieval: Instead of always pasting giant context, retrieve only necessary slices → indirectly reduces what needs caching.
✅ Summary:
You are correct — moving dynamic content to the end of prompts increases cache efficiency because the shared static prefix is reused across many requests. Combined with deterministic serialization and other caching techniques, this is a core scaling and cost optimization strategy for LLM-powered copilots and agents. For low-frequency personal use, the gains are negligible, but for large SaaS products serving millions of queries, it’s critical to profitability.
Would you like me to also draw out a diagram / schematic (like a visual flow of static vs dynamic prompt caching) so it’s easier to see how prefix reuse actually works?