Descriptions
Game Development and LLMs: Build Games with LangChain, 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 learn
- Design and code AI NPCs that use LangChain chains/tools for dialogue, hints, and quest logic
- Learn OpenAI Function Calling to let LLMs trigger in-game actions
- Implement semantic retrieval (RAG) for lore, quest text, and item descriptions; store and query embeddings to drive context-aware NPC responses.
- Learn to create tool wrappers and expose them to the LLM via LangChain with proper schemas and validation.
- Learn to build sequential and parallel chains using LangChain’s Runnable interfaces, combining LLM calls, tools, and post-processing.
- Create reusable PromptTemplate / ChatPromptTemplate with variables, few-shot examples, and output schemas
- Implement structured output (pydantic/JSON schema) and validate LLM responses inside chains.
- Implement end-to-end RAG pipelines: document loaders, chunking strategies, embeddings, retrievers, answer synthesis.
- Build tool-using agents (ReAct/Function Calling) with Tool/AgentExecutor to safely invoke game or app functions.
Who this course is for
- Beginner to intermediate Python learners who want a practical entry into AI—building real, interactive projects instead of just calling a single API.
- Students & career switchers in CS/IT who prefer hands-on projects to understand chains, prompt templates, memory, and retrieval.
- Aspiring or indie game developers who want to add intelligent NPC dialogue, dynamic quests, and data-driven behaviors using LangChain and OpenAI function calling.
Specificatoin of Game Development and LLMs: Build Games with LangChain
- Publisher : Udemy
- Teacher : Sachin Kafle
- Language : English
- Level : All Levels
- Duration : 7 hours and 43 minutes
Content of Game Development and LLMs: Build Games with LangChain
Requirements
- Beginner Python skills: variables, lists/dicts, functions, installing packages (pip)
- OpenAI account + API access: ability to create an API key and add a $5 prepaid top-up (typical practice runs in this course stay within that).
Pictures
Sample Clip
Installation Guide
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Quality: 1080p
Download Links
Downloadly
Rapidgator
Password file(s): www.downloadly.ir
File size
6.15 GB
