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, also often known as RAG, is a strong method to seek out relevant documents in a corpus of knowledge, which you then provide to an LLM to offer answers to user questions. Traditionally, RAG...

is on the core of AI infrastructure, powering multiple AI features from Retrieval-Augmented Generation (RAG) to agentic skills and long-term memory. Consequently, the demand for indexing large datasets is growing rapidly. For engineering...

for nearly a decade, and I’m often asked, “How will we know if our current AI setup is optimized?” The honest answer? A number of testing. Clear benchmarks help you measure improvements, compare...

, I’ve talked quite a bit about Reterival Augmented Generation (RAG). Specifically, I’ve covered the fundamentals of the RAG methodology, in addition to a bunch of relevant concepts, like chunking, embeddings, reranking, and retrieval...

Whether you’re a scientist brainstorming research ideas or a CEO hoping to...

structured data right into a RAG system, engineers often default to embedding raw JSON right into a vector database. The fact, nonetheless, is that this intuitive approach results in dramatically poor performance. Modern...

often use Mean Reciprocal Rank (MRR) and Mean Average Precision (MAP) to evaluate the standard of their rankings. On this post, we are going to discuss why (MAP) and (MRR) poorly aligned with modern user behavior in...

or Claude to “search the online,” it isn’t just answering from its training data. It’s calling a separate search system. Most individuals know that part. What’s less clear is how much traditional serps matter and...