automated-prompt-compilation-could-close-inference-theory-gap

OUT derived (depth 7)

Created 2026-06-21T12:50:29+00:00

The convergence of automated prompt engineering (LLMs generating human-quality prompts) with declarative LM pipeline compilation (DSPy optimizing multi-step LM programs) could close the systematic theory gap in prompt engineering — transforming inference-time control from craft-dependent tuning into a verifiable, compilable engineering discipline.

Justifications

SL — Automation + compilation = potential formalization path; gated because automating an architecturally exploitable interface amplifies rather than resolves injection vulnerability

Antecedents (all must be IN):

IN prompt-automation-paradox-exemplifies-systematic-theory-gap — Prompt engineering's paradox — automatable (LLMs generate prompts at human-level quality) yet fundamentally fragile (model-specific, 40%+ accuracy shifts from formatting) — is a microcosm of the field's systematic pattern where engineering capability outpaces theoretical understanding: the field can build tools that generate effective prompts without understanding why they work, mirroring its broader ability to deploy what it cannot formally specify.
IN dspy-declarative-lm-pipeline-compiler — DSPy is a framework that compiles declarative language model calls into self-improving optimized pipelines, representing the shift from manual to programmatic prompt engineering (Khattab, 2023, arXiv:2310.03714)

Unless (any of these IN defeats this justification):

IN prompt-injection-primary-security-concern — Prompt injection is the primary security concern for deployed LLM applications