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Lesson 1: LLMOps Infrastructure

🧠 The Concept (Explain Like I'm 5)

Running an AI application isn't just about calling the OpenAI API. You need memory (Vector Databases) so the AI remembers your company's documents, and traffic cops (AI Gateways) to prevent developers from racking up a huge API bill.

🏢 The Enterprise Context

  • RAG (Retrieval-Augmented Generation): Converting company PDFs into math vectors, storing them in Milvus/Qdrant, and injecting them into the LLM prompt.
  • AI Gateways (LiteLLM / Kong): Centralizes API keys, provides fallback models (e.g., if OpenAI is down, route to AWS Bedrock), and tracks cost per team.

🗺️ Visual Architecture: Enterprise LLM Flow

flowchart TD
    User["User Query"] --> App["Application Backend"]

    App -->|1. Search Context| VDB[("Vector Database<br/>(Milvus / Qdrant)")]
    VDB -->|2. Return nearest chunks| App

    App -->|3. LLM Request<br/>(Query + Context)| GW["AI Gateway<br/>(LiteLLM)"]

    GW -->|Route to primary| OpenAI["OpenAI (GPT-4)"]
    GW -.->|Fallback if down| AWS["AWS Bedrock (Claude)"]