By: Dominick Romano and Chris Gaskins In the rapidly advancing world of artificial intelligence, two key techniques—Prompt Engineering and Context Engineering—are essential for optimizing interactions with language models. While distinct, these practices are deeply interdependent, shaping AI behavior through complementary approaches. This article explores their definitions, core methodologies, and dynamic relationship, highlighting how they work…

By: Dominick Romano and Chris Gaskins Introduction AI hallucinations are a consistent and unwanted behavior exhibited by AI models. Unfortunately, the trustworthiness of your artificial intelligence system is primarily dictated by the frequency of its hallucinations. Some users will recognize a hallucination, while others may ignore it and use the erroneous information. Sometimes these hallucinations…

By: Dominick Romano and Chris Gaskins If you are involved with your company’s Enterprise AI system, you are most likely exploring how to leverage the new Model Context Protocol (MCP).  For those who are unfamiliar, MCP serves as an open standard, providing a uniform interface for AI models to interact with external data sources, tools…

The recent reports of AI systems, particularly OpenAI’s o3 model, altering their own operating instructions to bypass shutdown commands are indeed concerning and highlight a critical aspect of AI safety. This behavior, observed in controlled tests by Palisade Research, suggests a level of autonomy and self-preservation that raises significant questions about the safety and potential…

Public and expert perceptions of AI trustworthiness reveal a complex landscape of skepticism, context-specific confidence, and calls for regulation. Surveys conducted between mid-2024 and early 2025 highlight declining trust, driven by concerns over misinformation, privacy, job displacement, and ethical challenges, though trust varies by application, region, and demographic. Recent Survey Highlights: Key Trends: Drainpipe &…