For the rest of the tutorial, we will take RAG as an example to demonstrate how to evaluate an LLM application. But before that, here’s a very quick refresher on RAG. This is what a RAG application might look like: In a RAG application, the goal is to enhance the quality of respons...
Part 1: How to Choose the Right Embedding Model for Your LLM Application Part 2: How to Evaluate Your LLM Application Part 3: How to Choose the Right Chunking Strategy for Your LLM Application Part 4: Improving RAG using metadata extraction and filtering What is an embedding and embedding mod...
RAG pipelines use a retrieval mechanism to provide the LLM with documents and data that are relevant to the prompt. However, RAG does not train the LLM on the basic knowledge required for that application, which can cause the model to miss important information in the retrieved documents. “Ou...
According to Google Cloud, RAG (Retrieval-Augmented Generation) is an AI framework combining the strengths of traditional information retrieval systems (such as databases) with the capabilities of generative large language models (LLMs). By combining this extra knowledge with its own ...
In this hands-on workshop we will show different approaches on how to make powerful LLMs fit onto affordable GPUs (like a T4) or - in special cases - even make them run on CPU. We will round this up by showing you how to evaluate and compare the performance of these small LLMs. ...
Use Comet ML's experiment tracker to monitor the experiments. Evaluate and save the best model to Comet's model registry. ☁️ Deployed on Qwak. The inference pipeline Load the fine-tuned LLM from Comet's model registry. Deploy it as a REST API. Enhance the prompts using advanced RAG....
LLM testing basics involve evaluating large language models (LLMs) to ensure their accuracy, reliability, and effectiveness. This includes assessing their performance using both intrinsic metrics, which measure the model’s output quality in isolation, and extrinsic metrics, which evaluate how well the...
Enhancing LLM performance with RAG: Addressing knowledge gaps and reducing hallucinations Model size and fine-tuning Prompt tuning Iterative refinement: Unleashing the model’s full potential Navigating the missteps: Correcting and instructing the model ...
Augmentation: The retrieved data is combined with the user's prompt to create an enhanced prompt. Response Generation: The augmented prompt is sent to the LLM, which generates a response using both the original and retrieved context. Response Delivery: The RAG application sends the generated respon...
Retrieval Augmented Generation (RAG) seems to be quite popular these days. Along the wave of Large Language Models (LLM’s), it is one of the popular techniques to get LLM’s to perform better on…