Systems | Development | Analytics | API | Testing

RAG and Fine-Tuning are two prominent LLM customization approaches. While RAG involves providing external and dynamic resources to trained models, fine-tuning involves further training on specialized datasets, altering the model. Each approach can be used for different use cases. In this blog post, we explain each approach, compare the two and recommend when to use them and which pitfalls to avoid.

Test case is the backbone of any testing project. The art of software testing is to write the right test case. Firstly it’s not about how you write it, but rather what scenarios you are writing for. After that, we need to closely tie our test cases with test design and test execution. Let’s explore how to write test cases in the most strategic fashion.

After 20 years of dashboards, today’s line-of-business users expect more value from their analytics, and it’s up to your business to keep updated - or risk getting left behind. There was a time when product teams could purchase basic dashboards and data visualizations, and that was more than enough to satisfy the average user’s business intelligence (BI) and analytics needs. Today, however? Not so much.

Data has become the lifeblood of businesses across all industries. With the exponential growth of data collection and processing, the importance of data compliance has skyrocketed. And navigating this complex business landscape is vital for any organization handling sensitive information. 5 key takeaways from this post on mastering data compliance are.

Defect management is the systematic process of detecting, documenting, and resolving defects within software applications. A defect refers to any flaw or deviation from the expected behavior, which can arise from coding errors, incorrect logic, incomplete implementations, or unforeseen interactions between software components.

Data fabric is an architecture that integrates different data systems and tools. It provides unified access to data stored across various locations to organize, manage, and govern it without moving it to a central database or data warehouse or changing its format. Data fabric relies on metadata to ‘understand’ the data’s structure, lineage, and meaning across various sources. This information enables informed decision-making and optimized data usage.