Description
Dean Pleban and Liron Itzhakhi Allerhand explore what it really takes to move LLMs into production. They cover how to define clear requirements, prep data for RAG, engineer effective prompts, and evaluate model performance using concrete metrics. The conversation dives into managing sensitive data, avoiding leakage, and why crisp outputs and clear user intent matter. Plus: future trends like in-context learning and the decoupling of foundation models from vertical apps. Join our Discord community: https://discord.gg/tEYvqxwhah --- Timestamps: 00:00 Introduction 01:48 Phases of LLM Project Development 03:32 Defining the Problem 09:35 Data Preparation and Understanding 23:59 Multimodal RAG 26:28 Prompt Engineering & Model Selection 27:58 Model Fine-tuning & Customization 33:18 LLM as a Judge 38:58 Evaluating Model Performance and Handling Hallucinations 41:02 Using LLMs with sensitive data 45:24 Other ideas for model evaluation and guardrails 49:28 Recommendations for the audience ➡️ Liron Itzhaki Allerhand on LinkedIn – https://www.linkedin.com/in/liron-izhaki-allerhand-16579b4/ 🌐 Check Out Our Website! https://dagshub.com Social Links: ➡️ LinkedIn: https://www.linkedin.com/company/dagshub ➡️ Twitter: https://x.com/TheRealDAGsHub ➡️ Dean Pleban: https://x.com/DeanPlbn