LLM Architecture - SkrewAI (Page 2)

AI Agents

Inside AI Coding Agents: Architecture, Tools, and Agentic Loops

A technical deep dive into how AI coding agents work, from tool-calling mechanisms and agentic loops to planning systems and memory architectures that enable autonomous code generation.

LLM Architecture

KV Caching Explained: The LLM Optimization Behind Real-Time AI

Key-Value caching dramatically accelerates LLM inference by storing computed attention states. Understanding this technique is essential for building efficient AI video and synthetic media applications.

AI Agents

Kaggle's AI Agent Program: Key Learnings on Production Systems

Kaggle's intensive AI agent program reveals practical insights on building production-ready systems, covering orchestration patterns, tool integration, and deployment strategies for real-world applications.

AI Agents

AI Agents Waste 80% of Compute on Inter-Agent Communication

New research reveals multi-agent AI systems spend up to 80% of computational resources on coordination overhead rather than productive work, highlighting critical efficiency challenges in agentic architectures.

AI Agents

Structured Cognitive Loop: Merging Symbolic and Neural AI

New research introduces a cognitive architecture that bridges symbolic control and neural reasoning in LLM agents, offering a structured framework for more reliable and interpretable AI systems with explicit planning and execution phases.

LLM Architecture

Uncertainty Architecture for Robust LLM Applications

A technical framework for designing LLM applications that explicitly handle uncertainty, covering architectural patterns, confidence scoring, and system design principles for building more reliable AI systems.

Agentic AI

Small Language Models Dominate Agentic AI Systems

Small language models are outperforming larger counterparts in agentic AI workflows due to speed, cost efficiency, and specialized task performance. Technical analysis reveals why compact models excel at autonomous decision-making.

LLM Architecture

RAG vs Fine-Tuning: The LLM Architecture Decision

Comprehensive technical analysis of retrieval-augmented generation and fine-tuning strategies for LLMs, exploring when to use each approach, their technical trade-offs, and emerging hybrid architectures that combine both methodologies.

LLM Architecture

LLM Architecture Evolution: Transformers to MoR Explained

Deep dive into the technical progression of large language model architectures, from the foundational Transformer through Mixture of Experts to cutting-edge Mixture of Routers, examining how each innovation addresses scaling and efficiency challenges.