Welcome to the podcast exploring the fascinating world of large language models (LLMs) and their practical applications. In this series, we delve into how these powerful AI tools are being customised and fine-tuned for specific industries, moving beyond general capabilities. We examine techniques such as domain-adaptive pretraining to enhance LLMs for specialised tasks like chip design, leading to models that can even outperform state-of-the-art systems in niche areas.

We also explore the essential process of fine-tuning LLMs for enterprise use, offering practical guidance on data preparation for both text and code, estimating compute requirements, and choosing the right strategies like Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA) for efficient adaptation. Understand the critical steps involved in the seven-stage fine-tuning pipeline, from dataset curation to deployment and maintenance.

Furthermore, we investigate the role of Retrieval Augmented Generation (RAG) as an alternative or complement to fine-tuning, leveraging external knowledge to improve response quality. We look at how LLMs are being evaluated in specialised domains such as finance, law, climate, and cybersecurity, using targeted benchmarks to assess their performance in real-world scenarios. Finally, we touch upon innovative methods like test-time scaling to further boost the reasoning capabilities of these models. Join us as we unpack the technologies and best practices shaping the future of LLMs in diverse and demanding fields.