Knowledge Graphs and their limitations With the rapid growth of AI applications, Knowledge Graphs (KGs) have emerged as a foundational structure for representing knowledge in a machine-readable form. They organize information as triples—a head entity, a relation, and a tail entity—forming a graph-like structure where entities are nodes and relationships are edges. This representation allows…
Question: You’re deploying an LLM in production. Generating the first few tokens is fast, but as the sequence grows, each additional token takes progressively longer to generate—even though the model architecture and hardware remain the same. If compute isn’t the primary bottleneck, what inefficiency is causing this slowdown, and how would you redesign the inference…
Question: MoE fashions comprise way more parameters than Transformers, but they’ll run quicker at inference. How is that attainable? Difference between Transformers & Mixture of Experts (MoE) Transformers and Mixture of Experts (MoE) fashions share the identical spine structure—self-attention layers adopted by feed-forward layers—however they differ basically in how they use parameters and compute. Feed-Forward…
Question: “You’re an ML engineer at a health firm like Fitbit or Apple Health. Millions of customers generate delicate sensor information every single day — coronary heart charge, sleep cycles, step counts, exercise patterns, and so forth. You wish to construct a mannequin that predicts well being threat or recommends customized exercises. But resulting from…
In this half of the Interview Series, we’ll have a look at some of the frequent safety vulnerabilities in the Model Context Protocol (MCP) — a framework designed to let LLMs safely work together with exterior instruments and knowledge sources. While MCP brings construction and transparency to how fashions entry context, it additionally introduces new…
Every time you immediate an LLM, it doesn’t generate an entire reply — it builds the response one phrase (or token) at a time. At every step, the mannequin predicts the chance of what the subsequent token could possibly be based mostly on every part written to this point. But realizing possibilities alone isn’t sufficient…