Getting Started with Vericoding

Vericoding is a powerful approach to building high-assurance AI systems that combines neural networks with symbolic reasoning. In this post, we’ll explore the fundamentals and show you how to get started.

What is Vericoding?

Vericoding provides explicit correctness guarantees by encoding domain rules and invariants directly into an agent’s behavior. This constrains Large Language Models (LLMs) to valid logical spaces, ensuring that outputs meet your domain’s strictest requirements.

Key Benefits

1. Safety Properties: Verify behavior against specifications
2. Traceability: Every decision path is defensible
3. Reliability: Systems that know when to defer to humans

Example: Basic Vericoding Pattern

Here’s a simple example of how vericoding can be applied:

def verify_constraint(output: str, constraint: Callable) -> bool:
    """
    Verify that an output satisfies a given constraint.
    """
    try:
        return constraint(output)
    except Exception:
        return False

def safe_generate(prompt: str, constraints: list[Callable]) -> str:
    """
    Generate text that satisfies all constraints.
    """
    # Generate initial output
    output = llm.generate(prompt)
    
    # Verify constraints
    for constraint in constraints:
        if not verify_constraint(output, constraint):
            # Retry or defer to human
            return defer_to_human(prompt)
    
    return output

Comparison Table

Feature	Traditional AI	Vericoded AI
Correctness Guarantees	Probabilistic	Formal
Traceability	Limited	Complete
Safety Properties	Unverified	Verified
Human Deference	Manual	Automated

Next Steps

To implement vericoding in your systems:

1. Define Constraints: Identify the rules your system must follow
2. Implement Verification: Build verification functions
3. Integrate with LLMs: Connect verification to your AI pipeline
4. Test Thoroughly: Validate against edge cases

Conclusion

Vericoding enables us to build AI systems that are not just confident, but correct. By combining the power of neural networks with the rigor of formal methods, we can create systems suitable for high-stakes domains.

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