Vibe Coding at Scale: How Context Engineering Makes AI-Powered Development Actually Work

Vibe coding — describing what you want and letting AI write the code — is how a growing number of developers ship software in 2026. It works brilliantly for small scripts. It falls apart the moment your codebase hits 50 files. Here's the infrastructure that makes vibe coding work at production scale.

Key Takeaways

Vibe coding breaks at scale — context windows can't hold your entire codebase, so your AI starts hallucinating APIs that don't exist
Context engineering is the fix — deliver only the 5K tokens that matter out of 500K, every query
Snipara + RLM-Runtime — query your docs via MCP, execute code in Docker isolation, remember decisions across sessions
Works with any LLM — Claude Code, Cursor, VS Code, GPT — your existing AI subscription, supercharged with the right context

What Is Vibe Coding? The Shift from Writing to Directing

The term "vibe coding" was coined by Andrej Karpathy to describe a development style where you give up manual control and let the AI take the wheel. You describe intent in natural language. The AI writes the implementation. You review, iterate, and ship.

This isn't a novelty — it's becoming the default workflow for a significant segment of developers. Tools like Claude Code, Cursor, and GitHub Copilot have made LLM-assisted coding mainstream. But there's a gap between "AI writes a todo app" and "AI contributes to a 300-file production codebase."

The Vibe Coding Spectrum

Works Great

Small Scripts

New projects, prototypes, single-file utilities

Hit or Miss

Medium Apps

10-50 files, some patterns established

Breaks Down

Production Codebases

50+ files, established conventions, team standards

Where Vibe Coding Breaks: 4 Walls Every AI Developer Hits

If you've tried vibe coding on a real project, you've hit at least one of these walls:

🧠

Wall 1: Context Window Limits

Your codebase has 500K+ tokens. Claude's context window is 200K. You can't paste everything, so you manually copy files — and miss the ones that matter.

👻

Wall 2: Hallucinated APIs

Without seeing your actual code, the AI invents function signatures, imports from packages you don't use, and references files that don't exist.

🔄

Wall 3: No Memory Across Sessions

You spent an hour explaining your architecture. Next session? The AI has forgotten everything. You re-explain the same decisions every time.

⚠️

Wall 4: Unsafe Code Execution

The AI generates code but can't safely run it to verify. You become a human test runner, copy-pasting between the AI and your terminal.

These aren't AI limitations — they're infrastructure limitations. The LLM is capable. It just doesn't have the right context, memory, or execution environment.

The Missing Layer: Context Engineering for LLM-Powered Development

Context engineering is the discipline of delivering exactly the right information to an LLM at the right time. Not "dump everything and hope," but intelligent, query-aware retrieval that fits within token budgets.

Think of it as the difference between:

Approach	Tokens Sent	Relevance	Cost per Query
Paste entire codebase	500K	~2% relevant	$1.50
Manual file selection	20-50K	~40% relevant	$0.15
Context engineering	3-8K	~95% relevant	$0.02

Context engineering doesn't replace your LLM — it feeds it. You keep using Claude, GPT, Gemini, or whatever you prefer. The context layer ensures your LLM always has the right information to work with.

The Vibe Coder's Stack: Snipara + RLM-Runtime

Snipara is a context optimization layer that sits between your documentation and your LLM. RLM-Runtime adds safe code execution with Docker isolation. Together, they solve all four walls of vibe coding at scale.

Wall	Solution	How It Works
Context limits	Snipara MCP	Hybrid search compresses 500K to ~5K relevant tokens per query
Hallucinated APIs	Grounded context	Every answer cites real files, functions, and line numbers
No memory	Memory system	`rlm_remember` / `rlm_recall` persist decisions across sessions
Unsafe execution	RLM-Runtime	Docker-isolated code execution with full trajectory logging

Architecture

┌─────────────────────┐     ┌──────────────────┐     ┌──────────────────┐
│  Your Codebase      │────▶│  Snipara          │────▶│  Your LLM        │
│  (500K+ tokens)     │     │  (optimizes to    │     │  (Claude, GPT,   │
│                     │     │   ~5K tokens)     │     │   Gemini, etc.)  │
└─────────────────────┘     └──────────────────┘     └──────────────────┘
                                    │
                            ┌───────┴────────┐
                            │  RLM-Runtime   │
                            │  (Docker exec, │
                            │   trajectories)│
                            └────────────────┘

Workflow: From Natural Language Prompt to Production Code

Here's what vibe coding looks like with the full stack. Two modes depending on complexity:

LITE Mode: Quick Bug Fixes and Small Features

For tasks that touch fewer than 5 files — the bread and butter of daily development:

1You describe the task in plain English

"Fix the authentication timeout bug in the login flow"

2Snipara retrieves relevant context (~4K tokens)

Hybrid search finds auth middleware, session config, timeout constants — not your entire codebase

3Your LLM writes the fix with real context

No hallucinated imports. References actual file paths and function signatures from your codebase.

Bug fixed. Tests pass. Ship it.

FULL Mode: Complex Features with Chunked Implementation

For features that span 5+ files and require architectural decisions:

Context & Planning

rlm_shared_context → rlm_recall → rlm_plan → rlm_decompose

Load team standards, recall past decisions, generate execution plan, break into chunks

Chunk-by-Chunk Implementation

rlm_context_query → Read/Edit → RLM-Runtime (Docker)

For each chunk: query relevant context, implement, test in Docker isolation

Memory & Documentation

rlm_remember → rlm_store_summary → rlm_upload_document

Save decisions, store summaries, update docs — so next session picks up where you left off

Key insight: Each chunk only loads ~6K tokens of context instead of your entire codebase. Over a 6-chunk feature, that's 36K tokens total vs. 3M tokens if you pasted everything for each step.

Before and After: Vibe Coding with Context Engineering

Metric	Without Context Engineering	With Snipara + RLM-Runtime
Tokens per query	50-500K (manual paste)	3-8K (auto-retrieved)
Hallucination rate	High (invents APIs)	Near zero (grounded in real code)
Session continuity	None (re-explain every time)	Full (rlm_remember / rlm_recall)
Code execution safety	Manual copy-paste to terminal	Docker-isolated with trajectory logs
Team consistency	Every dev gets different patterns	Shared context enforces team standards
Cost per feature	$15-50 in tokens	$0.50-2 in tokens

Works with Claude Code, Cursor, VS Code, and Any MCP Client

Snipara is a context layer, not a replacement for your AI tools. It integrates via the Model Context Protocol (MCP) — an open standard supported by Claude Code, Cursor, Windsurf, Continue, and more.

Claude Code

Plugin with 14 commands + auto-setup hook

VS Code

Extension with 43 commands + Copilot LM tools

Cursor

MCP integration for chat + composer

Any MCP Client

Standard HTTP MCP server

You keep using your existing LLM subscription. Snipara handles context optimization separately — no API keys to share, no vendor lock-in.

Get Started in 30 Seconds (Free, No Credit Card)

Snipara's free plan includes 100 queries per month — enough to try it on a real project and see the difference.

Claude Code (Recommended)

/plugin marketplace add Snipara/snipara-claude

/snipara:quickstart

/snipara:lite-mode [your task]

VS Code

ext install snipara.snipara

Click "Sign in with GitHub" in the welcome notification

Any MCP Client (Manual)

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

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