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...
decompression, the calculations are performed as before in FP16 precision. The use of FP16 is acceptable since the LLMs still remain DRAM constrained so that the compute is not a bottleneck. FP16 also allows to retain the higher precision activations which overcomes loss...
in many applications, you will always use a human in the loop and are only using the LLM as an amplifier to carry out a part of the legwork. In such cases, specify an accuracy level that will make the LLM application acceptable for launch. You can then gather extra data to refine ...
As such, Gen AI has the potential to influence every element of the marketing strategy and every step of the strategic planning process. The accuracy of both generative and analytical AI is largely contingent on the quality and quantity of training data, though the choice of algorithm also can...
How to Measure Customer Perception Perception may be an intangible concept but it always has some visible hints or data that clearly indicate whether it’s positive or negative. And if you’re able to track those stats or feedback, you could be in a better position toexceed customer expectati...
Manual testing is a prudent measure until there are robust LLM testing platforms. Nikolaos Vasiloglou, VP of Research ML at RelationalAI, says, “There are no state-of-the-art platforms for systematic testing. When it comes to reliability and hallucination, a knowledge graph question-generating...
rather than when they are outraged, polarized and screen-addicted. Employers are customizing LLMs with their own data; they can use the opportunity to design LLMs in ways that increase curiosity and critical thinking in their employees, instead of encouraging overreliance and intellectual...
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, ...
enabling you to compare prompt responses across different model versions and observe differences in quality, accuracy, and consistency. You can also use evaluations to test your prompts and applications with the new model versions at any point in your LLMOps lifecycle...