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Code Generation Evaluations

An eval is a test harness that assesses code produced by an LLM. AVM enables you to generate code via an LLM, run it securely on a mesh of peer-operated nodes, and verify output correctness against expected results.

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Use Cases

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Web2: GPT-4 Test Suites

Validate LLM-generated functions across diverse test cases before production deployment.

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Web3: Solidity Validation

Test smart contract logic and ensure compliance with security standards.

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Scenario: Robust Code Validation

You need to ensure that functions transforming CSV to JSON handle edge cases and schema variations before deployment.

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Implementation: AVM-Powered Eval

1. Generate Code
   Prompt an LLM to produce transformation functions.
2. Execute Securely
   Use AVM’s runPython tool to run untrusted code in sandboxed containers.
3. Assert Results
   Compare outputs against predefined JSON schemas in the same workflow.

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Example (TypeScript)

import { runPythonTool } from "@avm-ai/avm-vercel-ai";
import { openai } from "@ai-sdk/openai";
import { generateText } from "ai";

const tool = runPythonTool();

async function evaluate(testCases: { input: string; expected: any }[]) {
  let passed = 0;
  for (const { input, expected } of testCases) {
    const { text: code } = await generateText({
      model: openai("gpt-4o"),
      prompt: `Write a Python function execute(input) that converts CSV to JSON`,
      tools: { runPython: tool },
    });
    const result = await tool.exec({ code, input: { data: input } });
    if (result.output.result === JSON.stringify(expected)) passed++;
  }
  console.log(`Passed ${passed}/${testCases.length}`);
}

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Next Steps

• Expand to multi-language evals (JS, Rust).
• Integrate into CI/CD pipelines via AVM API.
• Benchmark multiple LLMs under identical test harnesses.