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 are changing software development by integrating generative AI to automate repetitive tasks, enhance code quality, and streamline workflows.
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.
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.
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.
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.
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
With great enthusiasm, we unveil the Prem-1B series, an open-source, multipurpose large language model developed by Prem AI. This cutting-edge SLM offers the open community and enterprises the opportunity to harness capabilities that were once exclusively available through closed-model APIs, empowering them to build their advanced language models. The weights
Last week, we open-sourced PremSQL, a local first library that created customized Text-to-SQL solutions. When deploying RAG-based services (whether on documents or databases) for enterprises, it becomes crucial that the underlined data is not exposed to third-party APIs. With PremSQL, you can use our ready-made pipelines or customize and create
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?
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
Machine learning and generative AI offer potential, but high costs and complexity challenge ROI. This article explores strategic integration to maximize value, with Prem AI providing the tools to bridge the gap.
LLMs
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
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 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
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.
Prem Articles
Transformer inference powers tasks in NLP and vision, but is computationally intense, requiring optimizations. Large models like GPT-3 need extensive memory and FLOPs, with techniques like KV caching, quantization, and parallelism reducing costs.
News
AI tools like GitHub Copilot and Devin enhance developer productivity by automating repetitive tasks and offering coding assistance. However, they fall short in creativity, contextual understanding, and complex decision-making, ensuring developers remain indispensable.
Prem Articles
Open-source CodeLLMs like DeepSeek-Coder and Qwen2.5-Coder revolutionize code intelligence by offering repository-level training, multilingual support, and advanced features. These models rival proprietary solutions, fostering transparency, collaboration, and customization.
News
Generative AI is revolutionizing industries, reshaping how businesses operate and innovate. With rapid adoption and increased investments, organisations are leveraging AI for tasks like content creation, customer support, and software development.
Dictionary & Showcase
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.
Dictionary & Showcase
Chunking enhances Retrieval-Augmented Generation (RAG) by splitting large texts into manageable parts for efficient processing in language models. This technique supports accurate responses, maintains context, and enables fast, parallel processing.
Function calling is seen as revolutionary in chatbots, enhancing real-time capabilities. However, older NLU + Core bots handled many use cases effectively. This article explores the evolution of chatbots, weighing the hype of function calling against the proven efficiency of traditional methods.
LLMs
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.