Changes

Notable changes for the current cohort. The previous version of the course page described the 2025 flow, so this page focuses on what changed for 2026.

2026

Course structure

  • Module 1 is now Agentic RAG. It combines the RAG introduction with function calling and the agentic loop.
  • Agents are no longer a separate “Module A” after the dlt workshop. They are part of the main course flow from the first module.
  • Module 2 is now dedicated to Vector Search. It covers embeddings, semantic search, minsearch, sqlitesearch, PGVector, and optional ONNX-based embedders.
  • Module 3 is a new Orchestration module with Kestra. It covers context engineering, AI Copilot, RAG workflows, agentic workflows, multi-agent systems, and production considerations.
  • The dlt workshop remains in the 2026 course, now focused on pulling LLM traces from a monitoring service and preparing them for analytics.
  • Evaluation moved from Module 3 in 2025 to Module 4 in 2026. It now covers search evaluation, RAG answer evaluation, and the basics of agent evaluation.
  • Monitoring is now a full Module 5. In 2025, the main course page ended after evaluation. In 2026, monitoring is part of the main sequence.
  • Best Practices is Module 6 and End-to-End Project Example is Module 7. These are optional modules based on older material, but they are included in the 2026 syllabus as useful follow-up content.
  • The capstone project remains the final requirement for certificate eligibility.

Tooling

  • The course recommends modern Python and uv for dependency management.
  • Docker is more central because several modules need services such as PostgreSQL, PGVector, and monitoring dashboards.
  • The FAQ dataset now comes directly from the DataTalks.Club FAQ website.
  • Kestra is used for orchestration.
  • PostgreSQL, Streamlit, and Grafana are used for monitoring.

What stayed

  • The course is still practical and incremental.
  • RAG remains the main application pattern.
  • The FAQ assistant remains an important running example.
  • The course still does not require a GPU.
  • You can still complete the course on a normal laptop with a few dollars of OpenAI credit, or with a compatible alternative provider.

For the full curriculum, see Curriculum. For environment setup, see Environment Setup.