Data Preparation Best Practices for Fine Tuning

In this episode of The Prompt Desk podcast, hosts Bradley Arsenault and Justin Macorin dive deep into the world of fine-tuning large language models. They discuss:

• The evolution of data preparation techniques from traditional NLP to modern LLMs

• Strategies for creating high-quality datasets for fine-tuning

• The surprising effectiveness of small, well-curated datasets

• Best practices for aligning training data with production environments

• The importance of data quality and its impact on model performance

• Practical tips for engineers working on LLM fine-tuning projects
  

Whether you're a seasoned AI practitioner or just getting started with large language models, this episode offers valuable insights into the critical process of data preparation and fine-tuning. Join Brad and Justin as they share their expertise and help you navigate the challenges of building effective AI systems.
---
Continue listening to The Prompt Desk Podcast for everything LLM & GPT, Prompt Engineering, Generative AI, and LLM Security.
Check out PromptDesk.ai for an open-source prompt management tool.
Check out Brad’s AI Consultancy at bradleyarsenault.me
Add Justin Macorin and Bradley Arsenault on LinkedIn.

Hosted by Ausha. See ausha.co/privacy-policy for more information.