Nlp

Learn about evaluating the generation stage of RAG systems with advanced metrics for faithfulness, answer relevance, and context relevance. Discover how frameworks like RAGAs and ARES use large language models to overcome data bottlenecks, ensuring accurate and reliable AI-driven solutions.

Learn about evaluation metrics for the retrieval component of a RAG system. Discover how metrics like Precision@k, Recall@k, F1@k, MRR, and MAP can optimize your system's performance. Ensure your RAG pipeline delivers high-quality context for better-generated responses.

Discover how Retrieval-Augmented Generation (RAG) pipelines can fail and learn about strategies to enhance their robustness. Explore common failure modes, from data ingestion to synthesis, and how to address them. Equip yourself with the knowledge to improve your RAG system’s reliability and performance.

Learn about the fundamentals of RAG pipelines and its key components. Design OpenAI and LLAMA 3-based pipelines with a shared ingestor and Chroma vector database to run experiments on advanced RAG methods and failure modes.

Understand why ROUGE as a recall-based metric is suitable for evaluating summarization tasks. Learn how different variations of ROUGE are tailored to solve various challenges in summarization, and get an intuition for when to use which metric to evaluate your summarizations.

Discover how to evaluate machine translation models with the BLEU score. Understand its precision-based principles, calculations, and practical implementation for accurate sequence-to-sequence model assessment and implement it from scratch.

Discover how models for language tasks such as text classification, generation, or machine translation can be evaluated. In-depth exploration of essential classification metrics like precision, recall, and F1 score, and introductions to further metrics and benchmarks.