Detect and prevent LLM hallucinations: factuality checking, grounding verification, and off-brand content filtering.
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Detect fabricated facts, fake citations, and brand-policy violations before they reach users.
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System design fundamentals for AI engineers: client-server, APIs, latency percentiles, caching, load balancing, databases, and queues — each explained from zero, then mapped to how LLM systems change it.
The 60-term plain-English glossary for AI system design: LLM basics, retrieval, agents, infrastructure, reliability, scaling, cost, and safety — with deep-dive links into every guide section.
Build a support bot end to end: six iterations from one API call to a production-shaped architecture with retrieval, caching, model routing, guardrails, and observability — runnable code at every step.
Traditional vs agentic system design: the 7 dimensions that transform, anatomy of an agentic system, control flow paradigms, failure modes, and when to go agentic.
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