llm-twin-course
Learn free end-to-end production LLM & RAG system with best practices
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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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 Aug 17, 2026
- Provenance
- Not a fork · Organization account
- As of Aug 17, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
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Install
pip install llm-twin-course PyPIHow it fits your stack(10)
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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
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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 un...
For agents
This page has a .md twin and JSON over the API.