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Operating large language models in production: fine-tuning vs RAG, parameter-efficient tuning, evaluation, prompt management, vector databases, inference optimization, guardrails, and agents.
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Operating large language models in production: reading a model name, fine-tuning vs RAG, parameter-efficient tuning, evaluation, prompt management, vector databases, inference optimization, guardrails, and agents.
The discipline the industry pays most for and teaches least: error analysis, golden datasets, LLM-as-judge and its failure modes, validating a judge against human labels, evaluating RAG and agents, offline versus online, and benchmark CI. Methodology, not tool tours.
The security discipline for LLM and agent systems: prompt injection and the lethal trifecta, practical defenses, tool sandboxing and least privilege, agent identity and delegated authorization, and the OWASP Agentic threat model with red-teaming. Threat models and controls, not fear.