AI CONTEXT CACHING

WHY / WHY NOT / WHEN

Context caching sounds like an obvious win, until you try to build it too early.

THE TEMPTATION: FASTER, CHEAPER RESPONSES

Retrieval-Augmented Generation (RAG) applications are expensive. Each call to a language model incurs cost and latency. Naturally, teams reach for context caching, a way to reuse previously generated answers when a similar question arises.

On paper, it’s an intelligent optimization.
In practice, implementing context caching too early can derail development, inflate scope, and introduce risks long before it provides value.

THE HIDDEN COSTS OF CONTEXT CACHING

1. IT PRESUMES YOU KNOW WHAT USERS WILL ASK

In early stages, your users are still teaching you what matters. Questions vary wildly, phrasing shifts, and your system hasn't yet stabilized around repeatable interactions.

Caching at this point means storing:

• Non-representative prompts

• Poorly scoped requests

• Possibly irrelevant or even inappropriate data

Result: You pollute your cache and invite unpredictable behaviour later.

2. IT INTRODUCES PREMATURE COMPLEXITY

Effective caching isn't just key/value storage. You must:

• Normalize or semantically embed prompts

• Create cache invalidation rules

• Enforce domain boundaries

• Handle toxicity and off-topic requests

All of this assumes you’ve already done the hard work of content moderation, scope enforcement, and response validation, which you haven’t yet.

3. IT CREATES A FALSE SENSE OF MATURITY

A fast response from a cache gives the illusion that the system is working efficiently.
But caching the wrong thing, especially hallucinated or out-of-scope responses, can cause more harm than a slower, accurate answer.

Worse, it can delay identifying weaknesses in your RAG design, retrieval quality, or grounding materials.

THE BETTER ALTERNATIVE: PERSIST THREADS, ANALYZE OFFLINE

In the early stages, caching is not necessary. You need understanding.

Instead of rushing to optimize:

1. Persist all user interactions (threads)

2. Run offline analysis with tools like Azure ML or OpenAI embeddings

3. Cluster similar questions

4. Flag common patterns and inappropriate content

5. Decide what’s worth caching later

This lets your system evolve with actual usage data and puts you in control of what gets reused.

THE TWO DELIVERABLES OF EARLY QUESTION ANALYSIS

This offline approach leads to two critical outputs that drive future optimizations:

1. A Filter for Inappropriate or Unscoped Questions
By analyzing early interactions, you can train a recognizer to identify and block questions that fall outside your intended domain or raise red flags (e.g., toxicity, privacy violations).

2. A Cache of High-Quality, Repeatable Responses
With repeat interactions clustered and reviewed, you can build a small, curated cache of validated answers to frequently asked questions, ensuring they are safe, accurate, and aligned with your domain.

These two deliverables create a strong foundation for scalable RAG systems.

WHEN CACHING DOES MAKE SENSE

Once you see:

• Clear question patterns emerging

• High repeat rate on specific queries

• Stable domain boundaries

• Clean data classification and moderation

Then, and only then, context caching becomes a cost-effective, maintainable optimization.

FINAL THOUGHT

In early RAG development, caching is a scaling concern, not a startup concern.
Build understanding before you build optimizations.

Persist everything, learn from your users, and when the patterns stabilize, then cache with confidence.