Author: JAMIUL ISLAM - Page 4
Top Enterprise Use Cases for Large Language Models in 2025
In 2025, enterprises are using large language models to automate customer service, detect fraud, review contracts, and train employees. Success comes from focusing on accuracy, security, and data quality-not model size.
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.
Data Privacy for Large Language Models: Essential Principles and Real-World Controls
LLMs remember personal data they’re trained on, creating serious privacy risks. Learn the seven core principles and practical controls-like differential privacy and PII detection-that actually protect user data today.
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.
How Generative AI Boosts Supply Chain ROI Through Better Forecast Accuracy and Inventory Turns
Generative AI boosts supply chain ROI by improving forecast accuracy by 15-30% and increasing inventory turns through dynamic, real-time simulations. Companies like Lenovo and Unilever cut inventory costs by 20-25% using AI-driven planning.
Attribution Challenges in Generative AI ROI: How to Isolate AI Effects from Other Business Changes
Most companies can't prove their generative AI investments pay off-not because the tech fails, but because they can't isolate AI's impact from other changes. Learn how to measure true ROI with real-world methods.
Risk-Based App Categories: How to Classify Prototypes, Internal Tools, and External Products for Better Security
Learn how to classify apps into prototypes, internal tools, and external products based on risk to improve security, save resources, and avoid costly breaches. A practical guide for teams managing multiple applications.
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.
Measuring Developer Productivity with AI Coding Assistants: Throughput and Quality
AI coding assistants can boost developer throughput-but only if you track quality too. Learn how top companies measure real productivity gains and avoid hidden costs like technical debt and review bottlenecks.