The Pipes Behind the Prompt

While building a RAG-based assistant for my portfolio, I learned that prompt caching is not an optimization. It is plumbing.

I did not start with caching in mind. I was debugging latency spikes, inconsistent answers, and token usage that made no sense relative to traffic. The model was strong, retrieval was decent, and yet the system felt unstable. Eventually, I realized the problem was not intelligence. It was infrastructure.

This post is about the pipes behind the prompt, and why invisible systems matter more than clever prompts.

I Thought the Model Was the System

When I built my AI-Powered Portfolio Assistant, my focus was where most engineers start.

• Model choice
• Embedding quality
• Retrieval relevance
• Prompt design

The system worked well enough to demo. But once I started treating it like a real system instead of a demo, cracks appeared.

• Identical questions produced slightly different answers
• Latency varied without clear correlation to load
• Token usage climbed even when queries were repetitive
• Debugging hallucinations felt non-deterministic

At first, I blamed the model. Then I blamed retrieval. Eventually, I realized I was missing an entire layer.

Prompt Caching Is Plumbing, Not Optimization

The analogy that finally clicked for me came from plumbing.

When you turn on a faucet, you do not expect the water to be freshly generated. You expect it to flow through pipes that are already built, filtered, and pressurized.

Prompt caching plays the same role in AI systems.

• It prevents recomputation of identical work
• It stabilizes behavior across repeated requests
• It makes latency predictable
• It makes failures traceable

Without caching, every prompt is like digging a new well.

What Actually Gets Cached

One mistake I made early was thinking caching was a single decision. In practice, there are multiple cacheable layers in an AI system.

Static Prompt Segments

System instructions, formatting rules, safety constraints.

These rarely change, yet I was resending and reprocessing them on every request. Caching these immediately reduced token usage and improved consistency.

Retrieval Results

In my RAG pipeline, similar queries often retrieved the same chunks. Without caching, I was re-embedding, re-ranking, and reassembling context repeatedly.

Caching retrieval outputs did more for latency than switching models ever did.

Fully Resolved Prompts

For truly identical inputs, caching the final resolved prompt-response pair eliminated answer drift.

This was the turning point. Once identical inputs produced identical execution paths, I could finally reason about correctness.

Reliability Starts With Reproducibility

Here is the uncomfortable truth I learned.

You cannot evaluate what you cannot reproduce.

Before prompt caching:

• Hallucinations were hard to debug
• Latency benchmarks were noisy
• Regression tests were flaky

After prompt caching:

• Execution paths became deterministic
• Failures were traceable
• Token costs were predictable
• CI tests stopped flaking

Prompt caching turned the system from a black box into something I could reason about.

A Distributed Systems Parallel

This lesson reminded me of work I did on a fault-tolerant distributed log storage system.

Logs are append-only not because it is elegant, but because it is debuggable.

Prompt caching plays a similar role. It creates a record of intent.

You can ask:

• Have I seen this prompt before?
• Did it behave differently last time?
• What changed upstream?

Without caching, every request is ephemeral. With caching, the system gains memory.

Tradeoffs I Had to Accept

Caching is not free.

• Stale prompts can hide upstream bugs
• Cache invalidation is still hard
• Over-caching can freeze bad behavior

The solution was not aggressive caching. It was intentional caching.

I tied invalidation to:

• Prompt version changes
• Retrieval index updates
• Safety policy revisions

Caching became part of the design, not a patch.

How This Changed How I Build AI Systems

I no longer start with the question, “Which model should I use?”

I start with:

• What should be stable?
• What should be recomputed?
• What needs observability?
• Where do I want determinism?

Prompt caching forced me to see AI systems less as magical interfaces and more as pipelines.

And pipelines need good plumbing.

Closing Thought

Most users will never ask if you cache prompts.

They will not see it in demos. They will not praise it in reviews.

But they will feel it.

In faster responses. In consistent answers. In systems that fail loudly instead of mysteriously.

That is the work I enjoy most: making invisible systems trustworthy.