In this video, we dive into the strategies to combat hallucinations and biases in large language models (LLMs) in this insightful video. Learn about data cleaning, inference parameter tweaking, prompt engineering, and more advanced techniques to enhance the reliability and accuracy of your LLMs. Dive deep into practical applications with examples and stay ahead with the latest in AI technology!
► Jump on our free LLM course from the Gen AI 360 Foundational Model Certification (Built in collaboration with Activeloop, Towards AI, and the Intel Disruptor Initiative): https://learn.activeloop.ai/courses/llms/?utm_source=social&utm_medium=youtube&utm_campaign=llmcourse
With the great support of Cohere & Lambda.
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Chapters:
0:00 Hey! Tap the Thumbs Up button and Subscribe. You’ll learn a lot of cool stuff, I promise.
2:18 Tip 1: The importance of data
2:43 Tip 2: Tweak the inference parameters
3:30 Tip 3: Prompt engineering
4:02 Tip 4: RAG & Deep Memory
7:04 Tip 5: Fine-tuning
7:30 Tip 6: Constitutional AI
8:13 Stay up-to-date with new research and techniques (follow this channel! 😉 )
#ai #chatgpt #llm
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