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We investigate whether Large Language Models (e.g., GPT-4) can synthesizecorrect router configurations with reduced manual effort. We find GPT-4 worksvery badly by itself, producing promising draft configurations but withegregious errors in topology, syntax, and semantics. Our strategy, that we callVerified Prompt Programming, is to combine GPT-4 with verifiers, and uselocalized feedback from the verifier to automatically correct errors.Verification requires a specification and actionable localized feedback to beeffective. We show results for two use cases: translating from Cisco to Juniperconfigurations on a single router, and implementing no-transit policy onmultiple routers. While human input is still required, if we define theleverage as the number of automated prompts to the number of human prompts, ourexperiments show a leverage of 10X for Juniper translation, and 6X forimplementing no-transit policy, ending with verified configurations.

What do LLMs need to Synthesize Correct Router Configurations?