Generative AI | News, how-tos, features, reviews, and videos
Major update to Microsoft’s development platform, now available in a first preview, will focus on the development of cloud-native and AI-powered applications.
Large language models have become tech’s latest hammer, but not every problem is a nail. Answer these key questions before you commit.
Natural language generation, recommendation systems, and anomaly detection are good opportunities to create strong business value with genAI.
AI-generated code has transformed software development forever. That’s not necessarily good. A solid review process can shrink bloat and attack surfaces.
Should your company leverage a public large language model such as ChatGPT or your own private LLM? Understand the differences.
Code generation and copilots are just the beginning of new AI-enabled ways to develop, test, deploy, and maintain software.
Three powerful approaches have emerged to improve the reliability of large language models by developing a fact-checking layer to support them.
RAG is a pragmatic and effective approach to using large language models in the enterprise. Learn how it works, why we need it, and how to implement it with OpenAI and LangChain.
A lot of different skills are needed to create a genAI system that can do the most for your business. Here’s a description of the important roles.
With the right architecture, AI and automation can help drive entire business operations. Here’s a roadmap.