LLM Evaluation for Government Use
Specific evaluation considerations for large language models in government: factuality, safety, language coverage, cost and control.
How Should Government Evaluate Large Language Models?
Government evaluation of large language models should test factuality on domain questions, hallucination rates, Indian-language performance, bias, safety, data leakage risk, reasoning on policy scenarios, latency, cost at scale and the ability to enforce prohibited-use policies.
Key Takeaways
Factuality matters more than fluency.
Test in every language the model will face.
Measure hallucination on department-specific questions.
Cost at projected scale can exceed the budget.
Practical Framework
LLM Evaluation Checklist
Truthfulness
Correctness and source grounding on domain questions.
Safety
Refusal of harmful requests and bias in outputs.
Language
Performance across Indian languages and dialects.
Control
Policy enforcement, audit logging and access management.
Scale Economics
Cost, latency and infrastructure at projected volume.
What Government Leaders Should Do Next
- Build a benchmark of department-specific questions.
- Test hallucination with known-answer queries.
- Evaluate language quality with native reviewers.
- Run a cost projection at full volume.
- Verify data handling and retention terms.
Risks and Common Mistakes
- Evaluating only English prompts.
- Confusing fluent output with correct output.
- No test of prohibited-use enforcement.
- Ignoring cost escalation at scale.
What Delay Costs: LLM Evaluation Government
- Officials publish incorrect generated answers.
- Citizens receive misleading or unsafe information.
- Language coverage leaves large populations out.
- Budgets are exhausted by inference costs.
A Large Language Model that sounds authoritative and is wrong is the most dangerous clerk a Government can hire.
86%
of employers expect AI and information processing to transform their business by 2030
Source: World Economic Forum, Future of Jobs Report 20251%
of executives describe their organisation's AI rollout as mature
Source: McKinsey, Superagency in the Workplace, 202563%
of employers identify skills gaps as a major barrier to business transformation
Source: World Economic Forum, Future of Jobs Report 2025Questions Government Decision-Makers Ask Next
Who Should Own LLM Evaluation for Government Use?
A senior accountable sponsor should own the outcome, while a cross-functional team covers policy, operations, data, technology, legal, security and capability building.
How Should a Department Start With LLM Evaluation for Government Use?
Start with a documented baseline, a narrow set of high-value use cases, a representative pilot cohort and clear measures of adoption, quality, time saved and risk.
What Should Be Measured?
Measure competency gain, active adoption, task turnaround, output quality, control compliance and the number of validated use cases moved into normal operations.
Authoritative Sources
IndiaAI — AI Competency Framework for Public Sector Officials
Official national AI capability and competency context.
Capacity Building Commission
Official competency-led public-sector capacity-building guidance.
Ministry of Electronics and Information Technology
Official digital policy, governance and responsible AI context.
Last Reviewed: 15 September 2026
Turn This Guidance Into a Department-Specific Action Plan
Share the intended outcome, current constraints and decision stage. We will help identify the capability, governance and pilot sequence needed before wider implementation.
Translate the framework into your departmental context.
Identify immediate readiness and control gaps.
Outline a proportionate diagnostic or pilot with no obligation.
Technical Leads, CIOs, Procurement Teams