AI applications

Building applications on top of language models: retrieval, agents and chatbots.

1. 01

   RAG: retrieval-augmented generation explained

   What RAG is, how its two pipelines work, the building blocks and the decisions behind each, where it goes wrong, and the stack we use for it in our OpenShift lab.

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2. 02

   Agentic AI: how AI agents work

   What makes an AI system an agent, the reason-act-observe loop, tools and MCP, memory and planning, common design patterns, where agents fail, and how to run them safely on OpenShift.

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3. 03

   Building a production chatbot, end to end

   The architecture of an enterprise AI chatbot: every component from sign-in to model serving, what happens to one message, conversation memory, streaming, capacity, security, evaluation and deployment on OpenShift.

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4. 04

   Vector databases

   Milvus and pgvector.

   Coming soon