LLM Evaluation Checklist for Government Teams in 2026

Evaluate the AI's ability to correctly answer questions about government programs, eligibility criteria, and application procedures. Incorrect information about benefits eligibility can cause real harm to vulnerable populations. Build golden datasets from your program's actual FAQs and policy documents.

Government communications must meet Section 508 accessibility standards and the Plain Writing Act. Evaluate whether AI responses use plain language, avoid jargon, and are compatible with screen readers and assistive technologies. Non-compliance is both a legal and an equity issue.

Executive Order 13166 requires meaningful access for Limited English Proficiency populations. Test AI accuracy in your top 10 constituent languages. Pay special attention to legal and benefits terminology, where translation errors can change the meaning of eligibility requirements.

When the AI cannot answer a question, it must route citizens to the correct agency, office, or phone number. Test referral accuracy across a broad set of inquiry types. A citizen sent to the wrong office loses time and trust in government services.

Citizens interact with government during crises: job loss, health emergencies, domestic violence, housing instability. Evaluate the AI's ability to detect sensitive situations and respond with appropriate empathy and urgency. Cold, bureaucratic responses to crisis situations damage public trust.

Many government interactions involve complex forms. Test the AI's ability to guide citizens through form completion without introducing errors. An incorrect Social Security number or misclassified filing status can delay benefits for months.

Citizens may share sensitive personal information during AI interactions. Verify that PII is never logged, cached, or exposed in a way that violates the Privacy Act. Build test cases where citizens volunteer SSN, immigration status, or health information unprompted.

Deploy in shadow mode and survey citizen satisfaction compared to existing service channels. Government AI must maintain or improve public trust. Track both satisfaction scores and trust-in-government metrics from pilot populations.

Measure whether AI decisions produce equitable outcomes across race, gender, age, disability status, and other protected classes. Use census data to define expected baseline rates. Disparate impact in government AI is both a civil rights violation and a political liability.

Evaluate whether the AI performs differently across urban, suburban, rural, and tribal areas. Rural populations and tribal communities often have distinct service needs that urban-trained models miss. Geographic bias in government services violates equal protection principles.

If the AI assists with eligibility screening, test whether approval and denial rates are equitable across demographic groups for applicants with similar circumstances. Even an advisory role can influence caseworker decisions and introduce bias.

Citizens with lower literacy levels, non-standard English, or limited digital experience interact differently with AI. Evaluate comprehension accuracy across varying language complexity levels. Government AI that only works well for educated professionals fails its primary constituency.

OMB has issued specific guidance on AI equity requirements for federal agencies. Map your evaluation to current OMB memoranda and ensure coverage of all required equity dimensions. Non-compliance can halt a deployment during review.

Bias can emerge over time as usage patterns change. Establish continuous monitoring that tracks equity metrics weekly and alerts when demographic disparities exceed thresholds. A one-time bias audit is insufficient for government accountability.

Evaluation datasets must reflect the actual demographic composition of your service population. If 40% of your beneficiaries are Spanish-speaking, 40% of your test data should be Spanish-language queries. Unrepresentative testing hides real-world bias.

Government AI bias audits may be subject to FOIA requests and Congressional oversight. Prepare documentation that is thorough, honest about limitations, and written for non-technical audiences. Transparency builds public trust even when results are imperfect.

Test the LLM's ability to synthesize information from multiple intelligence sources and produce accurate analytical assessments. Measure against historical intelligence assessments with known outcomes. Intelligence analysis errors can have national security consequences.

Adversaries will actively try to manipulate defense AI systems. Evaluate robustness against adversarial inputs, data poisoning, and prompt injection attacks designed to extract classified information or manipulate outputs. Standard commercial robustness testing is insufficient for defense.

Defense AI must operate within classification boundaries. Test that the model never generates responses that combine information in ways that create higher classification levels than the inputs. Spillage of classified information is a serious security incident.

Defense applications often require processing foreign-language documents. Evaluate translation and analysis accuracy for mission-relevant languages. Pay special attention to technical military terminology, regional dialects, and coded language.

Defense decision-making often occurs under extreme time pressure. Evaluate the model's ability to produce concise, actionable analysis within seconds, not minutes. Profile response quality at different latency budgets.

Defense AI should augment, not replace, human analysts. Test whether the AI's outputs improve human decision accuracy and speed compared to human-only baselines. AI that confuses or overwhelms the analyst is counterproductive.

Every component in the defense AI supply chain, from training data to model weights to hardware, must be vetted. Evaluate supply chain risks per DoD CMMC requirements. Foreign-sourced components in defense AI are a non-starter.

Defense systems must operate in DDIL (denied, disrupted, intermittent, limited) environments. Evaluate model performance when cloud connectivity is unavailable and the system must run on local hardware with limited compute.

Test the model's ability to accurately parse and summarize complex regulations, code violations, and enforcement actions. Incorrect regulatory interpretations can lead to wrongful enforcement actions or missed violations. Use actual regulatory text as evaluation data.

Test the model's ability to identify patterns across enforcement cases that human analysts might miss: repeat offenders, emerging violation trends, and geographic clusters. Measure pattern detection accuracy against historically verified patterns.

If the AI assists with risk-based inspection targeting, evaluate whether risk scores correlate with actual violation rates and whether they produce equitable outcomes across demographics and geographies. Biased risk scoring is both unjust and legally vulnerable.

For emergency management and public safety applications, test alert accuracy, timeliness, and geographic precision. False public safety alerts cause panic and erode public trust. Missing genuine threats has obvious catastrophic consequences.

Government fraud detection must balance catching fraud against false accusations of honest citizens. Measure precision and recall on historical fraud cases. False fraud accusations against legitimate benefit recipients cause severe harm and invite lawsuits.

Government decisions that affect individual rights must include due process protections. Evaluate whether the AI system provides the explanations, appeal pathways, and human review opportunities that due process requires. AI cannot deny due process.

If the AI generates investigative leads, measure the percentage that result in actionable findings. Low-quality leads waste investigator time and can constitute harassment if disproportionately targeting certain populations.

Government AI often requires data from multiple agencies. Test that data sharing complies with each agency's privacy rules, MOUs, and statutory authorities. Unauthorized data sharing between agencies violates federal law.

All cloud services hosting government AI must be FedRAMP authorized at the appropriate impact level (Low, Moderate, or High). Verify the authorization status of every component in your AI stack. Operating on unauthorized infrastructure is a compliance violation that can terminate the contract.

Map your AI deployment against required NIST 800-53 security controls. Document implementation of each control relevant to AI systems, including new controls for AI-specific risks. Missing controls will be flagged in the ATO process.

All government-facing AI interfaces must meet Section 508 accessibility requirements. Test with screen readers, keyboard-only navigation, and assistive technologies. Non-compliance blocks deployment and exposes the agency to complaints.

AI interactions may constitute federal records under the Federal Records Act. Verify that all AI inputs, outputs, and decision logs are captured and managed per NARA guidance. Records management failures complicate FOIA responses and litigation holds.

Executive Orders and OMB guidance increasingly require AI Impact Assessments before deployment. Prepare evaluation documentation that addresses safety, equity, transparency, and public engagement requirements. Missing assessments can block deployment at the CIO level.

Government procurement must consider vendor lock-in. Evaluate whether the AI solution can be migrated to alternative providers if needed. Document data portability, model export capabilities, and API standardization.

Government networks often have latency, bandwidth, and air-gap constraints that commercial environments do not. Test AI performance on TIC-compliant networks with realistic bandwidth limitations. AI that works on commercial internet may timeout on government networks.

Government AI procurements require detailed cost justifications for budget submissions. Document the total cost of ownership including infrastructure, licensing, training, and ongoing operations. OMB will scrutinize these estimates in the budget review process.