Filippo Pedrazzini

News Featured

PREM and AWS Join Forces

PREM and AWS have joined forces to deliver an accelerated Generative AI developer experience on both AWS and the PREM platform. To kick off this milestone, PREM co-organized a GenAI Hackathon with AWS in Barcelona, where AWS's ProServe team collaborated with PREM's team to tackle complex

LLMs

Open Source Reasoning Models: Welcome Qwen QwQ and QvQ

Open-source reasoning models Qwen QwQ and QvQ represent a shift in AI from generation to structured reasoning. With transparency, multimodal capabilities, and fine-tuned performance, they set benchmarks in logical problem-solving, advancing industries like finance, healthcare, and education.

Fine-tuning Embeddings for Domain-Specific NLP

Fine-tuning embeddings are crucial for enhancing domain-specific NLP applications. General models may fall short in specialised fields like healthcare or law. By fine-tuning, models improve accuracy, relevance, and understanding of specific terminologies, ensuring better performance in niche tasks.

LLMs

Open Source Audio Models: Text-to-Speech and Speech-to-Text

Open-source frameworks like BASE TTS, ESPnet, and FunASR are transforming Text-to-Speech and Speech-to-Text technologies in 2025. With advancements in scalability, natural prosody, and low-resource deployment, these tools make high-quality speech AI accessible and customizable globally.

Prem Articles

Open Source Agentic Frameworks: LangGraph vs CrewAI & More

Open-source agentic frameworks like LangGraph, SmolAgents, CrewAI, PhiData, and Composio enable multi-agent AI systems with scalable, modular architectures. Key features include graph-based workflows, retrieval-augmented generation, hierarchical planning, and collaborative task allocation.

LLMs

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.

LLMs

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.

LLMs

Balancing LLM Costs and Performance: A Guide to Smart Deployment

Balancing LLM costs and performance requires strategies like dynamic model routing, hybrid deployment, and fine-tuning smaller models for specific tasks. Techniques such as token optimisation, caching, and leveraging open-source models help reduce expenses while maintaining efficiency.

LLMs

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

LLM Routing: AI Costs Optimisation Without Sacrificing Quality

LLM routing revolutionizes AI deployment by dynamically assigning tasks to the best models, reducing costs by up to 75% while maintaining high-quality outputs. Explore its role in customer support, content creation, and developer tools, and learn how it optimizes efficiency across industries.

LLMs

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

What is a MicroSaaS and How to Get Started with Making One Using AI

AI is transforming MicroSaaS by automating development, enabling personalization, and optimizing scaling. Tools like PREM-AI help developers create scalable, niche-focused solutions faster and with less effort. The future includes AI-native workflows and hyper-personalized features

LLMs

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.

LLMs

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.

AI-Powered Code Editors: Next Generation of Developer Productivity

AI-powered code editors are changing software development by integrating generative AI to automate repetitive tasks, enhance code quality, and streamline workflows.

RAGs

RAG vs Long-Context LLMs: Approaches for Real-World Applications

This article compares Retrieval-Augmented Generation (RAG) with Long-Context Large Language Models (LLMs) in managing extensive data and complex queries, highlighting key technical differences and applications.

LLMs

Large Language Models for Next-Generation Recommendation Systems

Large Language Models (LLMs) transform recommendation systems by addressing challenges like domain-specific limitations, cold-start issues, and explainability gaps. They enable personalized, explainable, and conversational recommendations through zero-shot learning and open-domain knowledge.

LLMs

Multimodal LLMs: Architecture, Techniques, and Use Cases

Multimodal Large Language Models (LLMs) integrate diverse data types—text, images, audio, and video—into unified frameworks, enabling advanced applications like image captioning, document analysis, and healthcare solutions.

News Featured

Lyra Drake's Public Debut at Art Basel 2024 – A New Frontier in AI and Art

At Art Basel 2024 in Miami Beach, Lyra Drake, a groundbreaking multidisciplinary artist, debuts her first major exhibition, Infinite Faith in a Finite World. This exhibit marks a transformative moment in the intersection of art and technology, powered by PREM AI's innovative AI solutions.

Open Source Release: Ayup: Facing the Deployment Nightmare

So you are given some Python project which creates an API endpoint which does some AI/ML inference. You need to run this on your own infra, a cloud VM or whatever. What do you do?

LLMs

Are Open-Source Models Good Now?

Open-source LLMs like Llama 3.1 and Prem-1B-SQL offer affordability, flexibility, and customization, rivaling closed-source models like GPT-4o in performance. With enhanced transparency and control, they are ideal for businesses seeking scalable and innovative AI solutions tailored to their needs

LLMs

How LLMs Are Transforming OCR for the Next Generation

Large Language Models (LLMs) are transforming OCR systems by improving text recognition accuracy, enabling better multilingual support, and seamlessly integrating vision and language understanding to tackle complex tasks like scene text recognition and handwritten content.

RAGs

Advanced RAG Methods: Simple, Hybrid, Agentic, Graph Explained

Discover Retrieval-Augmented Generation (RAG) methods: Simple RAG for basic tasks, Hybrid RAG combining retrieval techniques, AgenticRAG with modular multi-agent systems, and GraphRAG leveraging graph data. Each method offers unique strengths, tailored for tasks.

AI Agents Beginners Guide

AI agents are autonomous systems that perceive environments, make decisions, and learn over time. They range from simple reflex agents to advanced generative models. With applications in automation, creativity, and strategy, they enhance efficiency but face challenges like bias and data privacy

Anybody can be a Solopreneur in 2024, thanks to Generative AI

Generative AI empowers solopreneurs to launch and scale businesses with unprecedented speed and creativity, breaking barriers like coding expertise or capital needs. By leveraging AI tools, individuals can ideate, iterate, and execute efficiently, turning solo ventures into agile enterprises.