Learn to Evaluate LLMs and RAG Approaches

Dive deep into the world of Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) techniques! This video sheds light on the comprehensive factors to consider when assessing the performance and reliability of these models. Key takeaways include:

📏 Model Size and Complexity: Find the sweet spot between model size and computational efficiency.
📚 Training Data Quality: Uncover the importance of data diversity and its impact on model outputs.
⚖️ Bias and Fairness: Address biases to achieve fairer and more ethical AI results.
🌐 Ethical Considerations: Deploy LLMs responsibly, prioritizing user safety and ethical boundaries.
🛠️ Fine-tuning and Transfer Learning: Adapt LLMs effectively to specific tasks without compromising integrity.
🧩 Explainability: Understand the 'why' behind model responses, fostering trust and accountability.
🛡️ Robustness: Evaluate models against adversarial attacks and noisy inputs.
🔄 Continuous Improvement: Stay updated in the ever-evolving linguistic landscape for consistent performance.
Join us as we explore the intricacies of evaluating modern language models, ensuring their ethical, reliable, and optimized usage for various applications. Subscribe and tap into a reservoir of knowledge for Gen AI enthusiasts!

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