The importance of data quality in model training cannot be overstated. A high-quality, well-balanced dataset is the foundation upon which effective models are built. Without it, even the most sophisticated algorithms can produce unreliable or biased predictions. Before jumping into testing a model, it is crucial for data scientists and testers alike to have a clear understanding of the data […]
Improving Data Quality: Oversampling to Address Imbalanced Dataset
Build a semantic search tool using FAISS
This post provides an overview of implementing semantic search. Why? Because too often, we notice testers skip testing more complex features like autocomplete. This might be ok in most applications. But in domain specific applications, testing autocomplete capabilities of the product is important. Since testers can benefit from understanding implementation details, in this post, we will look at how autocomplete […]
Context-based question answering using LLM
Companies are going to want to query their own internal documents – especially with the rise of LLMs and improvements in AI. Qxf2 has already heard of several CEOs that want to use AI/ML models to glean insights from internal knowledge stores. What does this mean for a tester? Well, you can expect to test such systems in the coming […]