How to Reduce AI Token Usage by 95%

The Problem: You're Paying for Repetition

Every time you start a new AI coding session, you paste the same files. You explain the same architecture. You repeat the same decisions.

A typical prompt to Claude or ChatGPT looks like this:

I'm working on a Node.js project with the following structure:
- src/engine/ has the core logic (retrieval.ts, embedding.ts)
- src/store/ handles SQLite persistence (db.ts, nodes.ts)
- src/mcp/ is the MCP server (server.ts, tools/)
- The retrieval uses RUV scoring with a knapsack solver
- Embeddings use all-MiniLM-L6-v2 via @xenova/transformers

Now help me fix the retrieval timeout when the knowledge graph
exceeds 10,000 nodes. The knapsack solver is too slow.

That's ~850 tokens just for context. The actual question is 20 tokens. You're paying 42x more for setup than for the question itself.

Where Tokens Actually Go

Most tokens in a typical AI coding session fall into three categories:

1. File contents (40-60% of tokens)

You paste entire files because the AI needs to see the code. But the AI only needs the relevant functions, not the imports, not the helper functions, not the comments.

2. Architecture explanations (20-30% of tokens)

You explain the project structure, the tech stack, the design decisions. This information doesn't change between sessions, but you type it every time.

3. Conversation history (10-20% of tokens)

You summarize what happened in previous sessions. "We decided to use SQLite." "The bug was in the expiry check." "We're deferring connection pooling to Phase 2."

All of this is waste. The information exists in your codebase. It should be automatic.

The Solution: Persistent AI Memory

What if your AI coding tool already knew your project? What if you could just ask the question, and the relevant context was injected automatically?

That's what AI memory engines do. They maintain a persistent knowledge graph of your project and inject compressed, relevant context into every prompt.

Before (without memory):

Tokens sent:     4,200
Tokens useful:     183
Waste:           95.6%
Cost per prompt: $0.063

After (with memory):

Tokens sent:       183
Tokens useful:     183
Waste:             0%
Cost per prompt: $0.003

95.6% reduction. Same answer quality.

Real Numbers

At $0.015 per 1,000 tokens (Claude's rate), a developer making 50 prompts per day saves ~$90 per month. For a team of 10: $900/month.

How to Set It Up

# Install
npm install -g eidos-memory

# Set up (downloads model, configures shell)
eidos setup

# Use with any AI CLI
eidos wrap claude "fix the JWT expiry bug"
eidos wrap aider "refactor the auth module"
eidos wrap gemini "explain the retrieval algorithm"

Other Ways to Reduce Token Usage

• Use system prompts wisely — put project context in the system prompt, not every user message
• Be specific in your questions — "Fix the bug in auth.ts" beats "Something's broken"
• Use file references — tools like Cursor let you reference files with @filename
• Use MCP servers — MCP lets AI tools access your codebase directly
• Choose the right model — don't use Claude Opus for simple questions

The Bottom Line

Token waste is the hidden cost of AI coding. A memory engine eliminates this waste. You get the same answer quality, with 95% fewer tokens, at a fraction of the cost.