Model Evaluation | Hokstad Consulting

Model Evaluation

Blog posts in the Model Evaluation category

Progressive Perturbation for Backdoor Sample Screening

Flag potential poisoned training samples with controlled, label‑preserving perturbations, calibrated on clean data and routed for human review.

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AI Model Extraction Tests: Rate Limits and Alerts

Test rate limits, detection and user impact to measure, tune and retest defences against distributed AI model extraction.

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Spectral Signatures in Backdoor Attacks: Study Review

Explains spectral scoring to flag poisoned training samples, its limits, and why testing filter-and-retrain on your data is essential.

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AI Agent Fan-In: Resolving Conflicting Results

Match fan-in to conflict: rules for structured data, rank evidence for facts, use synthesis for interpretation, and surface uncertainty.

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Chain-of-Verification Prompts: 4 Steps for LLM Checks

Four-step CoVe method: draft claims, create neutral checks, run isolated evidence-based checks and revise with uncertainty noted.

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AI Agent Token Usage and Cost Tracking

Track every model call, normalise tokens, convert rates to GBP, attribute costs to tasks and monitor spend.

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Self-Consistency Prompts: How Majority Voting Works

Use self-consistency: sample varied LLM answers, normalise final responses, count votes and independently verify the leading answer.

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