If you've ever had an AI assistant "forget" something you told it 20 messages ago, you've experienced cache eviction. You just didn't call it that.
The context window isn't memory. It's a fixed-size cache. And like any cache, it works great when you manage it deliberately — and terribly when you don't.
The Cache Mental Model
Think about how you'd design a cache:
- Fixed capacity. You can store N items. When you add item N+1, something gets dropped.
- Access patterns matter. Recently accessed items are more likely to be relevant.
- Not everything belongs in the cache. You store hot data, not your entire database.
- Cache invalidation is hard. Stale data in the cache is worse than no data.
Now map that to a context window:
| Cache Concept | Context Window Equivalent |
|---|---|
| Cache size | Token limit (128K, 200K, etc.) |
| Cache entry | A message or document chunk |
| Cache miss | "I don't see that in our conversation" |
| Cache eviction | Older messages getting pushed out |
| Stale data | Outdated instructions that contradict current ones |
| Cache warming | Pasting project context at the start |
The metaphor holds surprisingly well. And the solutions from caching apply directly.
Pattern 1: Cache Warming (Seed Files)
In caching, you pre-load hot data on startup so the first requests are fast. In AI conversations, you do the same thing:
\`markdown
Project Context (load at session start)
- Language: TypeScript
- Framework: Express.js
- Database: PostgreSQL with Prisma ORM
- Style: functional, no classes
- Error handling: custom AppError class
- Tests: Vitest with in-memory DB
`
\
This is your warm cache. Every message the AI processes will have this context available. Without it, you're starting cold every time.
Rule: Keep your seed file under 500 tokens. It's a cache primer, not a documentation dump.
Pattern 2: Eviction-Aware Chunking
When you're working on a long task, the earliest messages get evicted first. Plan for this:
Don't: Have a 50-message conversation where message #3 contains critical constraints.
Do: Repeat critical constraints in a summary every 10-15 messages:
\`
Quick context refresh:
- We're building the /api/users endpoint
- Must validate email format
- Must check for duplicates before insert
- Return 409 on duplicate, not 400
`
\
This is the equivalent of refreshing a cache TTL. You're telling the system "this data is still hot."
Pattern 3: Selective Loading (Don't Cache Everything)
A common mistake: pasting your entire codebase into the context window. 128K tokens is a lot, right?
Wrong. Bigger cache ≠ better performance. In caching, loading everything causes:
- Slower lookups (the system has to scan more data)
- Eviction of actually-relevant items
- Memory pressure
Same with context windows:
- More tokens = slower responses and higher cost
- Irrelevant files push out relevant conversation history
- The AI's attention degrades with noise
Rule: Load only the files the AI needs for the current task. Three relevant files beat thirty tangentially-related ones.
Pattern 4: Cache Invalidation (Update, Don't Append)
The hardest problem in caching is invalidation: knowing when stored data is stale.
In AI conversations, stale data looks like this:
\
Message #5: "Use snake_case for all variables"
Message #12: "Actually, let's use camelCase"
Message #30: *AI uses snake_case*
\\
The AI saw both instructions. It might follow either one, especially if the older one is more prominently positioned.
Fix: When you change a decision, don't just add a new message. Restate the full current state:
\`
Updated style rules (replaces all previous):
- camelCase for variables and functions
- PascalCase for types and classes
- UPPER_SNAKE for constants
`
\
This is a cache invalidation + write. Clean and unambiguous.
Pattern 5: Cache Partitioning (Separate Concerns)
If you're working on frontend and backend in the same conversation, you're mixing cache partitions. The frontend context pollutes the backend work and vice versa.
Strategy: Use separate conversations for separate concerns, like separate cache namespaces:
- Conversation A: API endpoint design (backend context loaded)
- Conversation B: React component work (frontend context loaded)
- Conversation C: Database migration (schema context loaded)
Each conversation gets 100% of its cache budget for relevant context instead of splitting it three ways.
The Practical Checklist
Before starting any AI-assisted work:
- Warm the cache — Paste your seed file (project context, constraints, style guide)
- Load selectively — Only include files relevant to this task
- Plan for eviction — Summarize critical constraints every ~15 messages
- Invalidate cleanly — When decisions change, restate the full current state
- Partition when needed — Separate conversations for separate concerns
Why This Matters
Most advice about context windows focuses on the limit: "you have 128K tokens, use them wisely." That's like saying "you have 64GB of RAM, use it wisely" — technically true but not actionable.
The cache model gives you concrete strategies. You already know how to manage caches. Apply the same discipline to context windows and your AI interactions get dramatically more reliable.
How do you manage context across long AI sessions? Curious whether others use similar patterns or have different strategies.
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