ChatGPT

Learn how LLMs like GPT, Claude, and Llama process and generate human language at scale.

Design, version, and evaluate LLM prompts for production — system vs user roles, few-shot, chain-of-thought, and the limits that push you toward RAG, tools, and fine-tuning.

Foundation models, autoregressive and diffusion generation, and the engineering patterns required to ship reliable applications on probabilistic outputs.

Learn why LLMs use tokens, how BPE and SentencePiece work, and how token budgets govern context, latency, and cost.

Understand how LLMs invoke external tools and APIs—including computer use and browser use—to extend capabilities beyond text generation.

Engineer typed LLM tool intents with schema validation, authorization, timeouts, idempotency, observability, and safe production execution.

Engineer reliable LLM context budgets across instructions, evidence, history, tools, and output—with retrieval, truncation, summarization, and production observability.

Why free-text LLM output fails for machines, constrained decoding vs prompt-only JSON, OpenAI JSON Schema, Pydantic validation, and production patterns with Instructor-style retries.

OpenAI GPT family — GPT-5.6 Sol/Terra/Luna tiers, API capabilities, and workload-based production routing.

Systematically testing prompts — versioning, A/B comparison, format compliance, and regression detection in CI.

Reducing LLM spend — workload-based model routing, prompt compression, caching, batching, and token-aware context design.

Why LLMs hallucinate — next-token prediction is not truth-seeking — and how to mitigate with RAG, tools, structured outputs, abstention, detection, and verification.

Production chatbot architecture — conversation management, retrieval, guardrails, session state, and multi-channel deployment.

Structured path from LLM foundations to production AI — RAG, agents, evaluation, observability, and portfolio-ready engineering habits.