LLM learning hub
Start with how LLMs generate text, move into reliable application engineering, or study transformer and production systems in depth. Every route combines explanation, interactive mechanisms, hands-on evidence, and optional book-level study.
Three routes
New to LLMs
Understand how language models read, predict, generate, and fail—without assuming Python or advanced mathematics.
Explain core LLM behavior and build an evidence-based behaviour lab.
Software developer
Build typed, grounded, tested LLM applications with retrieval, tools, evaluations, and operational evidence.
Ship a grounded assistant with contracts, citations, evaluations, and recovery paths.
Advanced learner
Study transformer internals, adaptation, inference, agent runtimes, reliability, security, and production trade-offs.
Defend a production LLM architecture with measured quality, latency, cost, and risk.
Plain-language definitions for tokens, embeddings, RAG, MCP, attention, tool calls, and production concepts.
Browse termsEight focused comparisons, including RAG vs fine-tuning, agents vs workflows, and long context vs retrieval.
Open 8 guides