In this episode, Harpreet Sahota (Developer Relations Expert) and Andreas Welsch discuss augmenting your Generative AI model with new data. Harpreet shares his insights on going beyond the limitations of off-the-shelf Large Language Models (LLM) and provides valuable advice for listeners looking to create AI-based applications that generate tailored, traceable information.
Key topics:
- Learn about different techniques to get better results (prompting, fine-tuning, retrieval augmented generation)
- Get advice which technique to use when
- Understand the five steps for building RAG-based systems
- Find out how to improve RAG-based results
Listen to the full episode to hear how you can:
- Assess common LLM limitations such as knowledge cut-off and hallucinations
- Choose Retrieval Augmented Generation (RAG) to provide current, traceable data to your LLM
- Fine-tune a model to get tailored results based on adjusted weights and datasets
- Become aware of data requirements and chunking your data for optimal results
Watch this episode on YouTube:
https://youtu.be/pUzlhAFYyHI
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Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers.
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