Introduction
Testing for humans and AI
LLMs alone are not enough
LLMs have intristic limitations that make them unreliable.
Unpredictable Results
LLMs produce different outputs for the same prompt, complicating debugging and collaboration.
Surface-Level Understanding
AI can generate code that looks right but fails in practice, lacking true comprehension.
Missing Context
LLMs struggle with broader project context, creating friction in complex codebases.
Hidden Bugs
Tokenization quirks can introduce subtle issues that evade immediate detection.
Our Solution: AI + Guardrails
Benchify combines AI’s speed with deterministic safeguards to ensure code is reliable, secure, and maintainable.
Static Analysis
Automated scanning catches errors early running code.
Program Synthesis
Generate correct-by-construction code from specifications.
Formal Methods
Mathematical verification proves critical code sections work as intended.
Compiler Techniques
Advanced methods optimize and correct common AI errors automatically.
Tools for Confidence
Deploy with confidence whether you’re human or AI.