Author: Tess Rempel - Page 2

Optimizing Attention Patterns for Domain-Specific Large Language Models

Optimizing Attention Patterns for Domain-Specific Large Language Models

Optimizing attention patterns in domain-specific LLMs improves accuracy by focusing on key terms and relationships. LoRA fine-tuning cuts costs by 95% while boosting performance in healthcare, legal, and finance. But over-specialization can break general understanding.
Auditing and Traceability in Large Language Model Decisions: A Practical Guide for Compliance and Trust

Auditing and Traceability in Large Language Model Decisions: A Practical Guide for Compliance and Trust

Auditing and traceability in LLM decisions ensure ethical, legal, and transparent AI use. Learn how to implement governance frameworks, track model behavior, and comply with global regulations like the EU AI Act.
Cost per Action vs Cost per Token: Which LLM Pricing Model Saves You Money?

Cost per Action vs Cost per Token: Which LLM Pricing Model Saves You Money?

Cost per token dominates LLM pricing, but cost per action offers predictable, business-friendly billing. Learn how each model works, which fits your use case, and why per-action pricing is gaining ground in 2025.
Procurement of AI Coding as a Service: Contracts and SLAs in Government Agencies

Procurement of AI Coding as a Service: Contracts and SLAs in Government Agencies

AI Coding as a Service is now a key part of federal procurement, with strict SLAs, compliance standards, and measurable outcomes. Learn how agencies are using it to cut contract drafting time by 85% and improve code accuracy.
Model Lifecycle Management: Versioning, Deprecation, and Sunset Policies Explained

Model Lifecycle Management: Versioning, Deprecation, and Sunset Policies Explained

Learn how versioning, deprecation, and sunset policies keep AI models reliable, compliant, and safe. Real-world examples from finance, healthcare, and enterprise AI teams show why these practices aren’t optional anymore.