dcarpintero/ai-engineering
AI Engineering: Annotated NBs to dive into Self-Attention, In-Context Learning, RAG, Knowledge-Graphs, Fine-Tuning, Model Optimization, and many more.
This project provides practical guides and examples for engineers who build applications using large language models (LLMs). It helps you understand and apply techniques like self-attention, in-context learning, and retrieval-augmented generation (RAG) to improve LLM performance and reliability. You'll gain insights into advanced LLM features and best practices for developing robust AI solutions.
No commits in the last 6 months.
Use this if you are an AI engineer or machine learning practitioner looking to build and optimize real-world applications with large language models, leveraging advanced techniques and best practices.
Not ideal if you are an end-user seeking a ready-to-use LLM application or a researcher focused purely on theoretical advancements in AI, as this is geared towards practical implementation.
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Language
Jupyter Notebook
License
MPL-2.0
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Last pushed
Apr 02, 2025
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