AI Engineering you can
actually ship to production
Practical, production-grade AI systems engineering, taught through 63 open-source notebooks, weekly free live sessions, and a hands-on engineering community.
For AI engineers, ML engineers, backend engineers transitioning into AI, MLOps engineers, and technical founders building AI products.
AI & ML Engineers
Already building models. Want to get them into production reliably—eval, serving, observability, the works.
Backend Engineers → AI
Strong SWE foundations. Want to own RAG pipelines, agent architectures, and LLMOps instead of just calling the API.
MLOps Engineers
Managing infra for ML. Want to extend that to LLM systems—prompt versioning, drift detection, cost management.
Technical Founders
Building AI products. Need to make the right architectural calls early before the technical debt compounds.
RAG Engineering — 63-Notebook Curriculum
13 stages, 63 runnable notebooks, all built on a single shared corpus — so every technique is directly comparable. Naive RAG through enterprise production. No paywalls. No signups.
Evaluation Harness
Cross-notebook P@k baseline. Run after changing indexes or fusion.
Foundations
Naive RAG, chunking strategies, hybrid retrieval (FAISS + BM25 + RRF), reranking.
Query Intelligence
Query rewriting, multi-query expansion, HyDE (Hypothetical Document Embeddings).
Core Retrieval
Graph RAG, agentic retrieval with tool-using agents.
Models & Index
ColBERT late interaction, embedding benchmarking, quantized indexes.
Advanced Chunking
Contextual retrieval, proposition chunking, parent-child chunking.
Evaluation
RAGAS, DeepEval, SPLADE sparse retrieval, MMR diversity.
Generation-time RAG
FLARE, Self-RAG, LLMLingua compression, context ordering.
Deployment
Qdrant production setup, semantic caching, distributed tracing.
Production Engineering
Async pipelines, streaming, batch processing, failover, access control, audit logging, cost monitoring, data governance.
Advanced Topics
Multimodal RAG, temporal reasoning, federated retrieval, causal reasoning.
Enterprise Projects
Domain-specific RAG for legal, medical, financial, and technical documentation.
Production Apps & Infra
Customer support, code search, research assistants, incident response, scaling, security, compliance, privacy.
All notebooks share the same Orion Analytics corpus — so every technique is directly comparable. Runnable, free, no signups.
Open the Curriculum →How it works
Three entry points. All free to start.
WhatsApp Community
700+ engineers discussing RAG, agents, MLOps, and production architecture daily. Active discussions, shared notebooks, live Q&A.
Join free →Weekly Live Sessions
Weekly live engineering sessions announced in the WhatsApp group. Code-first, no slides, real system walkthroughs with live Q&A. Join the group to get session links.
Join WhatsApp for session links →Open-Source Notebooks
63 runnable notebooks on GitHub. Foundations → query intelligence → ColBERT → evaluation → FLARE → production engineering → enterprise projects.
Browse curriculum →Structured Courses
Go deeper with structured, project-based AI engineering courses.
What the curriculum actually covers
From the Engineering Blog
Production AI systems. No fluff, no hype—just implementation.
Come build in public with us.
700+ engineers in the WhatsApp community working through RAG, agents, MLOps, and production AI together. Weekly live sessions, shared notebooks, real architecture discussions.
If you're building AI systems and want to be around engineers doing the same—this is where that happens.
Join the Free WhatsApp Community