A practical guide to RAG evaluation metrics — faithfulness, context precision, context recall, and LLM-as-judge — and how to run evaluation in production using RAGAS, DeepEval, and LangSmith.
We will be using GPU accelerated Kernel for this tutorial as we would require a GPU to fine-tune BERT. If you want to understand how BERT works under the hood before diving into fine-tuning, check out my BERT Annotated Paper walkthrough. Prerequisites: Willingness to learn: Growth Mindset is all you need Some basic idea about Tensorflow/Keras Some Python to follow along with the code Initial Set Up Install TensorFlow and TensorFlow Model Garden import tensorflow as tf print(tf.version.VERSION) Cloning the Github Repo for tensorflow models ...