Model Evaluation | Hokstad Consulting

Model Evaluation

Blog posts in the Model Evaluation category

US Copyright Office: AI Authorship Report Explained

Explains when human input makes AI outputs copyrightable, and what records and disclosures to keep for registration.

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Activation Clustering vs Spectral Signatures

Use clustering to inspect suspicious groups and spectral analysis to rank suspect records — treat both as triage, not proof.

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AI Coding ROI Reports: Linking Tool Costs to Results

Measure AI coding impact: track full costs, compute net hours saved, separate cash savings from capacity, and report monthly.

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Multi-Agent Consensus: Debate vs Voting

Compare parallel voting and limited debate for multi-agent decisions—use voting for fixed outputs and bounded debate for disputes.

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Low-Error Poisoned Samples Challenge Auto-Encoders

Low reconstruction error doesn't prove data is clean — auto-encoder scores can hide label or backdoor poisoning.

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How to Trace Root Causes Across Multiple AI Agents

Find and confirm the first contract breach across multi-agent AI using traces, controlled replay, boundary tests and regression checks.

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Databricks Prompt Registry: Guide for LLM Teams

Version, test and release prompts safely: setup, aliases, application loading, approvals and rollback best practices.

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AI Agent Stagnation: How Progress Checks Catch Stalls

Measure verified results, enforce windowed progress checks and block further calls when AI agents show no measurable improvement.

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How to Measure AI Agent ROI: Baselines and Net Value

Measure AI-agent ROI by attributable cash: set a locked baseline, count full AI costs, separate time savings and model monthly payback.

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Zero Trust for AI: Microsegmentation Policy Blueprint

Microsegmentation blueprint for AI: map assets, deny-by-default, approve narrow agent access, assign owners and test policies.

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RAG Context Compression: Token Savings vs Lost Detail

Compare extractive trimming and generated summaries in RAG pipelines, balancing token cost, latency and evidence fidelity.

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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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