llm-twin-course
Learn free end-to-end production LLM & RAG system with best practices
GraphCanon updated 3d · GitHub synced 3d · 37 views this month
Decision brief
Provides a comprehensive, free course on building production-ready LLM & RAG systems, including 12 hands-on lessons.
Good fit when
- When seeking an extensive guide with practical implementation for setting up LLM and RAG systems using industry best practices.
- If you need to understand both the design choices and technical setup involved in deploying such systems on platforms like AWS.
Avoid when
- Avoid if you're looking for cost-free development, as it requires use of paid APIs from services like OpenAI and AWS.
- Not suitable if your primary goal is to learn theory only, as this repository emphasizes hands-on lessons over in-depth theoretical explanations.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (119d since push)
- As of 3d
- Provenance
- Not a fork · Organization account
- As of 3d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install llm-twin-courseHow it fits your stack(10)
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llm-course82k★second-brain-ai-assistant-course3.0k★
langflow153k★
vllm88k★
RagaAI-Catalyst16k★
transformers164k★
agenta4.4k★
ollama178k★
ragflow87k★llm-course82k★second-brain-ai-assistant-course3.0k★langflow153k★vllm88k★RagaAI-Catalyst16k★transformers164k★agenta4.4k★ollama178k★ragflow87k★Similar tools
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Evidence and technical details
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Overview
Provides a course for building an end-to-end production-ready Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) system, including source code and hands-on lessons.
Capability facts
- Deploy
- Self-host
Source: dockerfile:docker-compose.yml · Aug 17, 2026
- Docker
- Dockerfile present
Source: dockerfile:docker-compose.yml · Aug 17, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 17, 2026
Categories
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README
💰 Cost structure
All tools used throughout the course will stick to their free tier, except:
- OpenAI's API, which will cost ~$1
- AWS for fine-tuning and inference, which will cost < $10 depending on how much you play around with our scripts and your region.
🚀 Install & Usage
To understand how to install and run the LLM Twin code end-to-end, go to the INSTALL_AND_USAGE dedicated document.
[!NOTE] Even though you can run everything solely using the INSTALL_AND_USAGE dedicated document, we recommend that you read the articles to understand the LLM Twin system and design choices fully.
License
This course is an open-source project released under the MIT license. Thus, as long you distribute our LICENSE and acknowledge our work, you can safely clone or fork this project and use it as a source of inspiration for whatever you want (e.g., university projects, college degree projects, personal projects, etc.).
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For agents
This page has a .md twin and JSON over the API.