From 500K to 5K Tokens: The Math Behind Context Compression

Your LLM doesn't need 500,000 tokens. It needs the right 5,000. Here's the math behind context compression that reduces costs by 90% while improving answer quality.

The 500K Token Problem

A typical production codebase contains hundreds of thousands of tokens. Here's what we see across real projects:

Most developers try to feed their entire codebase to Claude or GPT. The result? Blown context windows, hallucinated answers, and API bills that make your CFO nervous.

The Cost of Raw Context

Let's do the math on feeding raw documentation to an LLM:

But that's not the worst part. When you dump 500K tokens into an LLM's context window, you're also getting:

How Context Compression Works

Context compression isn't about truncating your docs. It's about intelligent selection of the most relevant content.

The Three-Step Pipeline

Why Hybrid Search Matters

Pure vector search (embeddings only) misses exact matches. If your code has a function called validateAuthToken(), a semantic search for "authentication" might not find it.

Hybrid search combines:

• Keyword matching — Finds exact terms, function names, class names
• Semantic similarity — Finds conceptually related content
• Reciprocal rank fusion — Merges results from both approaches

Real Benchmarks

Here's what we measured across 50 real projects indexed with Snipara:

The RELP Engine: Recursive Context for Complex Queries

Some questions are too complex for a single retrieval pass. "How does the checkout flow handle failed payments and retry logic?" touches multiple files, concepts, and edge cases.

The RELP (Recursive Evaluation Loop Pipeline) engine handles this:

This achieves 100x effective context expansion without exceeding token budgets.

Why Grounded Answers Matter

Every answer from compressed context includes citations back to the source documentation. This means:

• Verifiable claims — Click through to see the actual code
• No hallucinations — The model can only cite what exists
• Audit trail — Know exactly where each fact came from

Getting Started

Ready to compress your context? Here's how to try Snipara:

{
  "mcpServers": {
    "snipara": {
      "type": "http",
      "url": "https://api.snipara.com/mcp/your-project",
      "headers": { "X-API-Key": "rlm_..." }
    }
  }
}