Learn Advanced Python by Building Games: LLMs and LangChain

About this course

In this project-based course, you'll use LangChain and the OpenAI API to transform game ideas into intelligent, interactive experiences. We'll begin by establishing the fundamentals that make LLM systems predictable and cost-effective: prompt templates that separate content from structure, chains that combine multiple steps into reliable workflows, callbacks for tracing and telemetry, and memory strategies that keep context tight without increasing token costs. Once these building blocks are ready, you'll move on to RAG (Retrieval-Augmented Generation), enabling NPCs to answer questions and make decisions based on your own game lore and documents. You'll also create agents that safely call in-game tools through validated, schema-driven function calls and strict allowlists. Throughout the course, you'll instrument your code, test behaviors deterministically, and implement guardrails to ensure outputs stay on policy. The course concludes with two platformer mini-games in which an agent and NPCs compete, adapting strategies using memory and retrieval to outplay each other. By the end, you'll have a reusable toolkit—prompts, chains, memory, RAG, and agents, along with portfolio-ready demos you can extend or deploy.

What you'll build & learn

OpenAI API fundamentals: streaming, reliable JSON/structured outputs, and function (tool) calling.

LangChain I/O & prompts: PromptTemplate, ChatPromptTemplate, few-shot patterns, and output validation.

Chains: sequential and router chains, batching/streaming, retries, and cost/latency tuning.

Callbacks & tracing: observe runs end-to-end and debug model/tool behavior with clear telemetry.

Memory: buffer, summary, and vector-backed memory with persistence, TTLs, and guardrails.

RAG: loaders → chunking → embeddings → retrievers → grounded answers with

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