We obsess about capabilities; we under-invest in correctness.
Context
I recently read about Forum AI – a startup that recruits domain experts to build benchmarks and trains AI “judges” to evaluate foundation models on high‑stakes topics such as geopolitics, finance, mental health and hiring. The company’s thesis is simple: fidelity in these domains requires expert-crafted evaluation, not just generic metrics or checkbox audits.
Why this matters for architects and CTOs
As a chief architect who has spent decades designing systems for enterprises and government, the Forum AI approach highlights a stubborn truth: model capability (can it generate fluent text?) is not the same as model competence (does it give the right answer in a complex, ambiguous context?). For enterprises adopting LLMs, that distinction is an architectural requirement, not an optional nicety.
Several structural implications follow:
– Trust is multi-dimensional. Accuracy is only one axis alongside provenance, bias, and epistemic humility. An answer that looks confident but lacks context is more dangerous than no answer at all. Architects must design systems that expose uncertainty and sources – not hide them behind polished prose.
– Domain expertise can’t be synthetic. Generic pretraining delivers broad fluency; it does not substitute for domain-specific judgment. If you are using AI for credit decisions, medical triage, or hiring, you must bake in subject-matter evaluation and edge‑case tests created with practitioners – not only data scientists.
– Compliance is not audit theatre. Checkbox audits and standardized benchmarks miss the tail risks and corner cases that create legal and reputational exposure. Real evaluation requires curated scenarios, adversarial testing, and clear SLAs tied to business outcomes.
Practical architecture advice (what to build)
1. Define a “stakes taxonomy.” Classify use-cases by potential harm and regulatory exposure. Treat high-stakes flows as first‑class citizens in your design: isolate them, add stronger logging, human review gates, and stricter deployment controls.
2. Build evaluators, not just unit tests. Create domain-specific benchmarks and automated “red teams” that run models through adversarial prompts and context-rich scenarios. Automate evaluation pipelines so each model version carries a clear scorecard on safety, bias, and provenance.
3. Implement provenance and uncertainty signals. Surface the model’s source material (when permissible), confidence estimates, and whether the response was generated, retrieved, or synthesized. For business workflows, require citations and human sign-off above configurable confidence thresholds.
4. Human-in-the-loop as default, not fallback. For nuanced decisions, route to human experts who have access to supporting context and clear escalation playbooks. Use AI to assist and summarize, not to make final, unreviewed decisions in high-stakes paths.
5. Legal and contractual alignment. Make responsibility explicit in SLAs and vendor contracts. Require vendors to show domain‑expert testing and post-deployment monitoring plans.
The India angle – a pragmatic bridge
India’s Digital Public Infrastructure and growing adoption of AI in banking, welfare, and health make these lessons directly relevant. When DPI services touch livelihoods, we cannot accept superficial audits. In my advisory work with STPI committees, I’ve seen how frugal engineering and rigorous testing must go together: local contexts create unique edge cases (language nuance, low-connectivity fallbacks, and socio-economic bias) that off‑the‑shelf evaluations miss.
Takeaways – a short checklist for leaders
– Treat “truthfulness” as a measurable KPI, not an aspiration.
– Invest in domain experts who can author benchmarks and adjudicate failures.
– Instrument models with provenance, confidence, and escalation hooks.
– Automate adversarial testing and monitor drift continuously.
– Align procurement and legal language to demand demonstrable domain evaluation.
Closing thought
We are at the point where platform speed and model capability can outpace our ability to govern consequences. The sensible path is not to slow innovation but to raise the bar for what counts as “production ready” – and to make expert judgment part of the pipeline, not an afterthought.
About the Author Sanjeev Sarma is the Founder Director of Webx Technologies Private Limited, a leading Technology Consulting firm with over two decades of experience. A seasoned technology strategist and Chief Software Architect, he specializes in Enterprise Software Architecture, Cloud-Native Applications, AI-Driven Platforms, and Mobile-First Solutions. Recognized as a “Technology Hero” by Microsoft for his pioneering work in e-Governance, Sanjeev actively advises state and central technology committees, including the Advisory Board for Software Technology Parks of India (STPI) across multiple Northeast Indian states. He is also the Managing Editor for Mahabahu.com, an international journal. Passionate about fostering innovation, he actively mentors aspiring entrepreneurs and leads transformative digital solutions for enterprises and government sectors from his base in Northeast India.
