The MLOps Podcast 👏 A Practical Approach to Building LLM Applications with Liron Itzhaki Allerhand
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May 13, 2025 In this engaging discussion, Liron Itzhaki Allerhand, a PhD in AI and former Microsoft expert, dives into the intricacies of bringing large language models to production. He emphasizes the importance of clear requirements and data preparation for effective LLMs. The conversation delves into prompt engineering, strategies to minimize hallucinations, and handling sensitive data. Liron also shares insights on future trends like in-context learning and the need for robust data leakage prevention, making it essential listening for AI enthusiasts.
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Define Clear LLM Requirements
- Define clear, concise requirements and avoid infinite lists to keep prompts manageable.
- Prepare concrete measurable criteria or good vs bad example answers to evaluate success clearly.
Avoid Prompt Overload
- Overloading a prompt with many requirements causes LLMs to lose track and hallucinate.
- Break complex problems into smaller sub-applications or agents for better LLM performance.
Prepare and Chunk Data Carefully
- Perform exploratory data analysis to check data quality, distribution, and requirement mismatches.
- Use natural chunking with overlap for RAG and include relevant, fresh context to reduce hallucinations.