Build a support agent for technical tickets where vector search alone returns similar but incomplete answers. Design a GraphRAG flow with session context, fresh ingestion, guarded generation, fallback escalation, and monitoring so unresolved cases reach humans with useful context.
Learn how to design a context pipeline for an LLM-backed support assistant so growing prompts do not cause high latency, runaway token costs, stale answers, or hallucinations.
Build a central gateway for LLM calls so product services do not hardcode providers, retries, caching, routing, safety checks, or cost controls. The workflow covers fallbacks, streaming, RAG, evaluation, and privacy guardrails for production systems.
Design an AI learning platform where LLMs generate vetted content offline, while the live lesson API selects personalized questions from approved data, uses warm Redis buffers, handles cold starts, and keeps grading deterministic.
Design an e-commerce search flow that uses LLMs for query understanding without making every request wait on them. Build offline enrichment, hybrid retrieval, tiered caching, fallback routing, ranking, and quality gates for relevance and latency.
Learn how regression models predict continuous values, how linear, polynomial, regularized, and advanced regressors work, and how to evaluate them with a practical Python workflow.
Learn how to map software problems to machine learning approaches, compare supervised and unsupervised learning, understand common algorithm families, and plan a practical workflow from data preparation to optimization and evaluation.