AI Agent Data Pipelines & Knowledge Systems

AI Agent Data Pipelines & Knowledge Systems

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Build the data infrastructure that makes AI agents actually useful — RAG, embeddings, and knowledge management.

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AI Agent Data Pipelines & Knowledge Systems

Concept & Motivation

An AI agent is only as good as the data it can access. The base model knows the internet; it doesn’t know your company. The gap between “impressive demo on public data” and “useful agent on our data” is a data pipeline problem.

Most agent projects fail not because the AI isn’t capable, but because the data infrastructure isn’t there. Documents are scattered across SharePoint, email, and shared drives. Knowledge lives in people’s heads. Structured data sits in databases that agents can’t query. The agent is powerful but blind.

This course teaches you to build the data layer that makes agents see: retrieval-augmented generation (RAG), embedding pipelines, knowledge bases, and the data architecture that turns AI agents from demos into daily tools.

What You’ll Learn

  • RAG architecture from scratch — document ingestion, chunking strategies, embedding models, vector storage, and retrieval. The full pipeline, designed for production quality
  • Chunking strategies that actually work — fixed-size, semantic, recursive, and document-structure-aware chunking. Why chunk quality matters more than model quality for most use cases
  • Embedding models — OpenAI, Cohere, open-source (e5, BGE), and self-hosted options. How to choose, how to benchmark, and when to fine-tune
  • Vector databases — Qdrant, Pinecone, pgvector, ChromaDB. Architecture trade-offs, scaling characteristics, and when a simple SQLite solution is enough
  • Hybrid retrieval — combining vector search with keyword search, metadata filtering, and SQL queries. Why pure vector search fails for structured queries and how to fix it
  • Knowledge graph integration — when relationships between entities matter more than document similarity. Lightweight knowledge graphs that complement RAG
  • Multi-source pipelines — ingesting from SharePoint, Google Drive, Confluence, databases, APIs, and email. The plumbing that enterprise RAG actually requires
  • Quality metrics — retrieval precision, answer groundedness, hallucination detection. How to measure whether your RAG pipeline is working and where it’s failing
  • Private and secure architectures — self-hosted embeddings, on-premise vector stores, access-controlled retrieval, and data classification. Building knowledge systems where sensitive data stays on your infrastructure

Who This Is For

  • Data engineers and architects building the data infrastructure for AI agent deployments
  • AI engineers implementing RAG pipelines for production agent systems
  • Technical leads making architecture decisions about knowledge management and data access
  • Information architects designing how organizational knowledge connects to AI agents

Programming experience and basic understanding of databases required. Experience with Python or similar scripting language recommended.

Format & Duration

2-day workshop (on-site). Day 1: RAG architecture, chunking, embeddings, and vector storage with guided implementation exercises. Day 2: multi-source pipelines, quality evaluation, and a build session where participants design and prototype a RAG pipeline for their organization’s data.

What Makes This Course Different

RAG tutorials are everywhere. Production RAG architecture guidance is rare. This course covers the engineering decisions that tutorials skip: how to handle document updates, how to manage embedding drift, how to scale retrieval without destroying latency, and how to evaluate quality systematically.

The exercises use real document collections, not toy datasets. And the architectures are designed for enterprise constraints — on-premise requirements, access control, and data classification.

Q & A

Learn more about what we do

It's an AI-focused data engineering course. Traditional data pipelines move data between systems. Agent data pipelines make data *usable by AI agents* — which means embedding, chunking, indexing, and retrieval design. If you've built ETL pipelines, the concepts transfer; the design priorities are different.
Retrieval-Augmented Generation (RAG) lets agents access your organization's specific knowledge — documents, policies, product data, customer records — instead of relying only on what the base model knows. Without RAG, agents hallucinate about your business. With good RAG, they give accurate, grounded answers. The difference between a useful agent and a liability is often the quality of your RAG pipeline.
Not always. We cover vector databases (Qdrant, Pinecone, pgvector), but also simpler approaches: keyword search, hybrid retrieval, and SQLite-based solutions for smaller datasets. The right choice depends on your data volume, query patterns, and infrastructure constraints. We help you make that decision during the course.
One module specifically covers private RAG architectures: self-hosted embedding models, on-premise vector stores, access-controlled retrieval, and data classification for agent inputs. You can build a knowledge system where sensitive documents never leave your infrastructure — we show you how.
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AI Agent Data Pipelines & Knowledge Systems AI Agent Data Pipelines & Knowledge Systems Concept & Motivation What You’ll Learn Who This Is For Format & Duration What Makes This Course Different Q & A Learn more about what we do