Code Generation Evaluations

Auto-benchmark LLM output for correctness and safety using AVM

Objective: Auto-benchmark LLM output for correctness and safety by executing it in AVM’s secure, isolated environments.

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.

​Use Cases

​Web2: GPT-4 Test Suites

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

​Web3: Solidity Validation

Test smart contract logic and ensure compliance with security standards.

​Scenario: Robust Code Validation

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

​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.