Learn how SuperAnnotate helped Twelve Labs build quality training datasets & cut multimodal AI development time by 50%.

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.

Learn how GumGum, a leader in Contextual Intelligence, fine-tunes LMs with SuperAnnotate and Databricks.

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

Gain insights from a webinar on LLM fine-tuning for business needs, featuring experts from SuperAnnotate and Databricks.

Discover how SuperAnnotate optimized REM People’s workflow, enabling them to maintain their competitive edge in a fast-paced market.

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.