Blog

Explore how Retrieval-Augmented Generation systems are vulnerable to Prompt Injection and Corpus Poisoning, and why deployed pipelines face persistent risks.

Explore traditional information retrieval models like BoW, TF-IDF, and BM25, their key strengths and limitations, and why they remain central to search systems today. We also touch on the rise of semantic search and how it complements these methods for more accurate results.

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.

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.

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.

Deconvolutions, transposed convolutions, fractionally strided convolutions, and what to do about artifacts generated by them.