LLM

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

Boost LLM performance with OpenAI's reinforcement fine-tuning (ReFT): A fusion of reinforcement learning and fine-tuning for superior reasoning skills.

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

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

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.

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

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

AI hallucinations uncovered: exploring the causes, risks, and prevention of hallucinations in LLMs.

Enhance LLM fine-tuning with a focus on data quality. Learn practical steps for data improvement using SuperAnnotate’s platform.