Alignment and Assurance: Preparing AI Models for Production

navdeepgill · 3 weeks ago

Part 3 of the “Inside the AI SDLC at ServiceNow” Series

Once a model has been trained and fine-tuned, it’s tempting to think the hard part is over. But at ServiceNow, a model isn’t ready for production until it’s been aligned with human preferences, validated for platform use, and reviewed by stakeholders across legal, product, and engineering teams.

This third post in our four-part series explores how ServiceNow ensures that tuned models are not just performant, but safe, responsible, and ready to deliver value across the platform.

Why Alignment Matters

A well-tuned model is designed to be both technically accurate and trustworthy, ensuring it meets the highest standards of usability. To bridge this gap, models undergo an alignment phase where their behavior is refined to reflect human expectations better.

This step helps the model:

Respond in a tone consistent with ServiceNow's brand
Avoid unsafe, biased, or inappropriate outputs
Follow instructions reliably in real-world use cases

Alignment ensures that the model doesn't just produce correct answers, it produces helpful, safe, and contextually appropriate responses.

Human Preference Optimization

The alignment process often includes reinforcement learning based on human feedback or synthetic preference signals. Here's how it works:

Instruction-response pairs are created or sourced, with examples of preferred and non-preferred completions
Labels are assigned through human judgment or model-assisted voting
The model is trained to prioritize high-quality, preferred outputs

The result is a model that better understands nuanced tasks and responds in a way that aligns with end-user expectations.

Validation Testing: Trust Through Evidence

Once aligned, models undergo extensive validation to ensure they meet the quality and safety standards required for production. This includes:

Regression Testing
1. Focused evaluations on prioritized tasks to confirm that:
  1. No critical capabilities were lost during training
  2. Quality has improved (or at least held steady) relative to previous versions
Multilingual & Domain Testing
1. Validating that models maintain quality across supported languages and domain-specific use cases.
Business Unit Testing
1. If applicable, internal business units evaluate whether model changes affect the performance of their applications. Where regressions are found, mitigations like prompt tuning or fallback strategies may be applied.
Final Quality Review
1. Cross-functional stakeholders, including quality engineering, product, and AI teams, review the outcomes against release goals and platform expectations.

Risk and Compliance Review

Before a model can be considered for release, a pre-launch legal and risk review is performed. This ensures:

The model and its training data comply with relevant licensing and regulatory standards
Any new use cases are reviewed for ethical and contractual risk
All aspects of the model's performance are thoroughly documented to ensure a comprehensive understanding

This review ensures the model's release aligns with ServiceNow's governance framework and meets safety and compliance standards.

The Go/No-Go Decision

Once all validations and reviews are complete, a formal Go/No-Go decision is made in collaboration with executive stakeholders from product, engineering, and quality assurance. This is where trade-offs are discussed openly, and the final decision is made based on:

Whether all release goals have been met
Confirmation of no major regressions from previous versions
Consideration of risk, value, and long-term maintainability

Only one model is selected to move into the deployment phase. Others may be retained for future reference or ongoing experimentation.

Conclusion: Confidence, Not Just Capability

At ServiceNow, the goal isn’t just to build powerful AI models, it’s to build models that customers and teams can trust. Alignment and validation are the safeguards that ensure every model released to production is not only technically strong but also aligned with the values, standards, and expectations of our platform and community.

In the next and final post of this series, we’ll explore how production-ready models are deployed, monitored, and managed throughout their lifecycle.

Stay tuned for Blog 4: “From Lab to Live: Deploying and Managing AI at Scale.”

If you missed the earlier post(s), be sure to check them out:

Download our Responsible AI whitepaper to explore our approach in more depth.