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.

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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/

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Links:

➡️ LinkedIn: https://www.linkedin.com/company/dagshub

➡️ Twitter: https://x.com/TheRealDAGsHub

➡️ Dean Pleban: https://x.com/DeanPlbn

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