_{Optimizing LLM Code & Cost with Myrkat}

The core challenge for AI coding tools (LLMs) is providing them with sufficient, relevant context to generate accurate and efficient code. Myrkat addresses this by providing context that is surgically precise and grounded in real-world application behavior.

_{Better Code Quality}

Myrkat improves code quality by providing LLMs with context that goes beyond basic file content or local code structure.

• Behavioral Context: Myrkat analyzes the codebase alongside live production data to understand application flows and business-critical use cases. When an engineer uses an LLM to generate or modify code, Myrkat's MCP server can feed the LLM not just the existing code block, but also a summary of:
  
  • Which use cases this specific code block is part of.
  • The typical data path and dependencies of that use case in production.
  • Specific performance metrics and any recent regressions associated with that flow.
• Preventing Regressions: By providing the LLM with production behavior data, the model is guided toward generating code that is consistent with the desired application flow and less likely to introduce subtle, hard-to-find bugs or performance degradations (regressions). The resulting code is contextually correct and production-aware.

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_{Reduced Token Consumption}

The more code and context you provide an LLM, the higher the token cost and the longer the latency. Myrkat significantly reduces token usage by providing high-value, highly compressed context.

• Surgical Context Injection: Instead of passing the entire file, related files, or a vast log/trace history into the prompt (which inflates token count), Myrkat's MCP server distills the necessary information into a compact summary focusing only on the specific use case and dependencies relevant to the code being changed.
• Efficiency and Cost Savings: This targeted, relevant context dramatically reduces the size of the input prompt, leading directly to lower token consumption. For teams integrating LLMs deeply into their workflow, this translates into significant cost savings on API calls and faster response times from the LLM.

In essence, Myrkat acts as an intelligent context filter and behavioral translator, ensuring the LLM receives the most impactful, least verbose information needed to generate high-quality, production-ready code.

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