LLM

Discover how LLM agents combine planning, memory, and tool use to solve complex LLM problems.

Explore how reinforcement learning from AI feedback (RLAIF) streamlines AI training, enhancing efficiency without extensive human input.

Explore how retrieval-augmented generation (RAG) remains vital in long-context LLMs, adapting with techniques like OP-RAG for enhanced precision.

Discover the role of synthetic data in fine-tuning LLMs, offering a blend of scale, privacy, and real-world complexity for AI training.

Discover LLM pruning & distillation: Transform LLMs into streamlined SLMs using the MINITRON approach, significantly reducing training costs and resources.

Discover how multi-agent LLMs enhance AI by letting expert agents collaborate on complex tasks. Learn their key benefits, frameworks, and applications.

Learn how Direct Preference Optimization (DPO) streamlines language model tuning, offering a stable and cost-effective alternative to traditional RLHF methods.

RAG vs. fine-tuning: Which suits your LLM? Learn how to choose between retrieval augmented generation and fine-tuning for better model performance.

Understand Retrieval Augmented Generation (RAG): A groundbreaking AI that merges GPT-like generation with data retrieval for accurate, up-to-date responses.

Explore LLM red teaming with our complete guide, offering expert tips and effective security measures to enhance the reliability of large language models.

Explore RLHF's transformative role in making LLMs more attuned to human preferences, enhancing AI interactions for a more intuitive future.

Discover how Small Language Models (SLMs) transform AI in 2024, offering powerful yet efficient solutions with fewer resources.