LLM features have the potential to significantly improve the user experience, however, they are expensive and can impact the performance of the product. Hence, it is critical to measure the user value they add to justify any added costs. While a product-level utilit...
These phases have quite different performance profiles. The Prefill Phase requires just one invocation of the LM, requiring the fetch of all the parameters of the model once from the DRAM, and reuses it m times to process all the m tokens in the prompt. With sufficie...
We use the answer_similarity and answer_correctness metrics to measure the overall performance of the RAG chain. The evaluation shows that the RAG chain produces an answer similarity of 0.8873 and an answer correctness of 0.5922 on our dataset. The correctness seems a bit low so let’s ...
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...
I use this four-stage process to systematically understand and fix errors in LLM applications. Stage 1: Preparation Before fixing errors, you should be able to measure them. In this stage, you will formulate the target task in a way that allows you to track the performance of the model. ...
NDCG is a common metric to measure the performance of retrieval systems. A higher NDCG indicates an embedding model that is better at ranking relevant items higher in the list of retrieved results. Model Size: Size of the embedding model (in GB). It gives an idea of the computational ...
Scholars should explore when and how to measure, manage, and mitigate bias in outputs. Beyond managing the LLM, two possible approaches entail altering the algorithm or applying human augmentation, or some combination of both. Specifically, firms could manage bias by including costly human ...
How to measure assistant quality When evaluating conversational assistants, we need to consider various aspects of their performance. These aspects include: Tool interactions: Verifying that the assistant correctly interacts with tools to fulfil user requests, such as booking a room in a hotel or orde...
LLMs are designed to give users answers in response to prompts, which can lead to overreliance and reduce the discernment of users. Professor Pattie Maes and other researchers at the MIT Media Lab have found that if LLMs are designed to first engage users to think about a proble...
Finally, it is critical to measure the queue length of your message broker and ensure that too much “back pressure” isn’t being created in the system. If you find that your tasks are sitting in a queued state for a long period of time and the queue length is consistently growing, ...