Category: Artificial Intelligence - Page 3
Checkpoint Averaging and EMA: How to Stabilize Large Language Model Training
Checkpoint averaging and EMA stabilize large language model training by combining multiple model states to reduce noise and improve generalization. Learn how to implement them, when to use them, and why they're now essential for models over 1B parameters.
Data Residency Considerations for Global LLM Deployments
Data residency for global LLM deployments ensures personal data stays within legal borders. Learn how GDPR, PIPL, and other laws force companies to choose between cloud AI, hybrid systems, or local small models-and the real costs of each.
Citations and Sources in Large Language Models: What They Can and Cannot Do
LLMs can generate convincing citations, but most are fake. Learn why AI hallucinates sources, how to spot them, and what you must do to avoid being misled by AI-generated references in research.
Fine-Tuning for Faithfulness in Generative AI: Supervised and Preference Approaches
Fine-tuning generative AI for faithfulness reduces hallucinations by preserving reasoning integrity. Supervised methods are fast but risky; preference-based approaches like RLHF improve trustworthiness at higher cost. QLoRA offers the best balance for most teams.
Continuous Security Testing for Large Language Model Platforms: Protect AI Systems from Real-Time Threats
Continuous security testing for LLM platforms detects real-time threats like prompt injection and data leaks. Unlike static tests, it runs automatically after every model update, catching vulnerabilities before attackers exploit them.
Governance Models for Generative AI: Councils, Policies, and Accountability
Governance models for generative AI-councils, policies, and accountability-are no longer optional. Learn how leading organizations reduce risk, accelerate deployment, and build trust with real-world frameworks and data from 2025.