Continual Learning: How AI Models Stay Smarter Over Time
Continual learning keeps AI models adaptive and up to date. It reduces model drift, preserves accuracy, and ensures continuous improvement through data-driven retraining and evaluation cycles.
Topic
LLMs
Discover every aspect of Large Language model development lifecycle, form training to deployment, evaluation and innovation trends with our dedicated resources.
How to Succeed with Custom Reasoning Models?
Custom reasoning models enable multi-step reasoning beyond LLMs. Learn how PremAI helps enterprises build scalable, explainable, high-performance AI.
SLM vs LoRA LLM: Edge Deployment and Fine-Tuning Compared
Fine-tuning is critical for adapting language models to real-world tasks. This blog compares SLM full fine-tuning with LoRA for LLMs, highlighting strengths, challenges, and edge deployment strategies. Learn how PremAI enables efficient, scalable, and enterprise-ready AI solutions.
Fine-Tuning & Small Language Models
The era of GPT-4's dominance is ending as diverse, specialized language models take the stage. Companies now favor both open-source and proprietary models tailored to specific tasks, moving away from one-size-fits-all solutions. Small Language Models (SLMs) offer efficiency for niche needs.
Multilingual LLMs: Progress, Challenges, and Future Directions
Multilingual LLMs face challenges like cross-lingual knowledge barriers, data imbalances, and performance disparities in low-resource languages. Key advancements include multilingual fine-tuning, retrieval-augmented generation (RAG), and adaptive architectures.
Are Agentic Frameworks an Overkill?
Agentic frameworks offer advanced adaptability and automation but come with high complexity and cost. This article explores their benefits, limitations, and practical alternatives to help you decide whether they are the right solution for your AI and automation needs.
Edge Deployment of Language Models: Are They Ready?
Edge deployment of LLMs promises low latency, privacy, and real-time insights. This article explores the challenges, cutting-edge solutions, and future opportunities to make edge-based AI a reality across industries like healthcare, robotics, and IoT
Is the current AI agents ecosystem again a Hype?
The AI agent ecosystem is rapidly evolving, blending autonomy and adaptability to transform industries. This analysis delves into their development, real-world applications, and challenges, highlighting advancements in decision-making, efficiency, and integration.
LLMs Evaluation: Benchmarks, Challenges, and Future Trends
The evaluation of Large Language Models (LLMs) focuses on benchmarks, scalability, ethical challenges, and multimodal testing. Dynamic frameworks and emerging trends drive robust, adaptive AI performance, ensuring safer, efficient deployment in sensitive fields like healthcare, finance, and law.
LLM Observability: Practices, Tools, and Trends
Explore LLM observability with this comprehensive guide. Understand metrics, logs, traces, and tools like Langfuse and SigNoz. Learn best practices, handle production challenges, and stay ahead with trends like multi-modal monitoring and AI-driven anomaly detection.