Insights
Research, best practices, and insights on AI product development, evaluation, governance, and operations. Articles focus on measurable outcomes and production-grade implementation.
Before deploying AI, you need baseline metrics. We explain how to establish meaningful baselines for deflection, cycle time, error rates, and other KPIs. Without baselines, you can't measure improvement—and you can't justify investment.
Evaluation harnesses are critical for preventing quality degradation. We share patterns for building evaluation suites that catch regressions before they impact users, including task-specific tests, edge case coverage, and automated quality gates.
Visibility shouldn't be an afterthought—it's a core product feature. We explain how the AI Control Tower provides real-time dashboards, evaluation suites, and operational monitoring that enable continuous improvement and risk management.
Not every task needs GPT-4. We share strategies for routing requests to the right model based on complexity, cost, and latency requirements. Intelligent routing can reduce costs by 40-60% while maintaining quality.
Red-team testing is essential for catching policy violations, safety issues, and compliance risks. We provide playbooks for systematic red-team testing, including prompt injection, jailbreaking, and bias detection.
Task success rates matter, but business KPIs matter more. We explain how to track deflection, cycle time, conversion lift, and revenue impact—and how to attribute improvements to AI deployments.
AI agents that call tools need robust error handling. We share patterns for handling tool failures, timeouts, and retries—ensuring agents degrade gracefully and maintain user trust.
A/B testing is critical for optimizing AI systems. We explain how to set up A/B tests for models, prompts, and routing strategies—with statistical rigor and business impact tracking.
Choosing the right approach for domain-specific requirements. We provide a framework for deciding when fine-tuning, RAG, or prompt engineering is optimal—based on cost, performance, and data availability.
Executive readouts need to balance technical detail with business impact. We share templates and frameworks for monthly readouts that communicate ROI, risk, and optimization opportunities effectively.
Intelligent caching can dramatically reduce costs and improve latency. We explain caching patterns for LLM applications, including semantic caching, result caching, and cache invalidation strategies.
Systematic documentation of failure modes accelerates learning and prevents repeat incidents. We share how to build failure-mode catalogs and use them for regression prevention and system improvement.
New articles and insights are published regularly. Book a call to discuss specific topics or request content on areas of interest.