OTUS opens advanced LLM course on ReAct, Reflection, and RAG

OTUS, a Russian online education provider, has opened registration for an advanced large language model course launching July 23, 2026. The curriculum targets data scientists, ML engineers, and NLP practitioners looking to build production-grade LLM systems that reason, self-correct, and integrate external knowledge sources.

The course centers on three architectural patterns increasingly common in enterprise deployments. ReAct (Reasoning and Acting) teaches models to interleave thought processes with tool use, letting them break complex queries into steps and call external functions mid-inference. Reflection adds a verification layer where the model critiques its own outputs before finalizing responses, catching factual errors and logical gaps. RAG (Retrieval-Augmented Generation) wires LLMs to document stores, vector databases, or APIs, grounding answers in current information rather than relying solely on training data.

Production deployment patterns

The program runs evenings Moscow time and assumes familiarity with transformer architectures and Python ML stacks. A free introductory session on July 23 at 8 PM MSK walks through a case study combining all three patterns in a single workflow—prompting a model to retrieve context, reason through a multi-step problem, then validate its conclusion before returning output. Registration is open on the OTUS platform.

OTUS positions the course as practical training for teams deploying LLMs in regulated industries or customer-facing applications where hallucination risk and answer reliability matter more than raw benchmark scores. The three-pattern stack has become standard in enterprise AI deployments over the past year, particularly in finance, legal research, and technical support where incorrect answers carry measurable cost.