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The diagram contrasts a Base LLM, which merely predicts the next word from training data and can continue stories or answer simple facts but may generate unsafe text, with an Instruction‑Tuned LLM that is fine‑tuned via RLHF to understand and follow commands, delivering more accurate, useful, and safe responses.
This article explains the fundamentals of distributed consistency, covering strong, weak, and eventual consistency, the CAP theorem, BASE model, and how ACID principles relate to modern distributed systems, helping engineers balance availability and data integrity in large‑scale architectures.
This article provides a comprehensive overview of distributed system fundamentals—including CAP and BASE theories—while detailing typical pitfalls and mitigation strategies for message queues, distributed caches, database sharding, and distributed transactions, helping engineers decide when and how to adopt distributed architectures.
This article explains the CAP theorem, the trade‑offs among consistency, availability, and partition tolerance, introduces the BASE model and its properties, and details how different database systems implement various consistency guarantees such as strong, eventual, and causal consistency.