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LLM

3 articles

A struck out one dimensional slider labeled RAG versus fine tuning sits above a two axis diagram where retrieval moves along a knowledge axis and fine tuning moves along a behavior axis toward a corner point marked both.

RAG vs fine-tuning: why the comparison mostly doesn't make sense

RAG and fine-tuning solve different problems: knowledge boundaries versus behavior. Treating them as interchangeable wastes months of engineering.

RAGFine-TuningLLMAI EngineeringProduction AI
A funnel diagram narrowing through five dashed defense gates, with capabilities like exec code and silent execute struck out above the funnel wall and a narrow read only outlet at the far right.

How to defend AI systems against prompt injection

Prompt injection is not a bug you patch once. It is a threat model you design against. Here is what actually reduces risk in production systems.

SecurityLLMPrompt InjectionProduction
Cache waterfall diagram with three stacked layers labeled prefix cache, exact cache, and semantic cache, each showing a stale risk readout, plus a dashed bypass lane routing user context and live state directly to the model call.

Caching strategies for LLM applications

LLM responses are expensive, slow, and often repeated. Here is how to cache them without building a system that silently returns stale answers.

LLMCachingPerformanceCost Optimization