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← Series hub Next → Answer-first: Self-hosting Small Language Models (2B–14B) with Go hybrid routing and vLLM serving reduces enterprise API costs by up to 65%, eliminates PII privacy risks, and delivers specialized domain performance matching 100B+ models. For the past two years, enterprise AI adoption has been dominated by a singular architectural pattern: API integration with massive, closed-source models (Frontier LLMs). While this API-Centric model allows for rapid prototyping, it becomes a severe liability when scaled to production workloads handling sensitive company data. ...

Prerequisite: Familiarity with the concepts introduced in Part 1 — Context Engineering Ddd. Review it first if the terminology in this part is unfamiliar. Answer-first: Enterprise RAG architectures replace naive text chunking with multi-stage data pipelines combining layout-aware global scanning, hybrid dense-sparse vector search, and cross-encoder context reranking. This architecture eliminates table slicing hallucinations, enforces metadata access controls, and cuts retrieval prompt token overhead by 70% while maintaining sub-400ms end-to-end query latency. ...

Prerequisite: Familiarity with the concepts introduced in Part 5 — Operating Model. Review it first if the terminology in this part is unfamiliar. Answer-first: AI Observability applies Site Reliability Engineering (SRE) principles to generative AI systems through OpenTelemetry gen_ai semantic conventions, distributed prompt tracing, and continuous evaluation pipelines. This framework detects silent model drift, monitors LLM API token expenses, and reduces failure detection time from weeks to under five minutes. ...