• Managing Hallucinations and Inaccurate Outputs in LLMs: Challenges and Solutions

    Oct 21 2024 ,  By: Fenil Dholariya

    LLMs are powerful but often generate inaccurate outputs, known as hallucinations. These errors can be harmful in fields like healthcare and law. Solutions like data curation, knowledge bases, and fact-checking systems help manage these inaccuracies, improving LLM reliability. Discover how addressing hallucinations is key to the future of AI.

    Read More

  • Demystifying AI: Overcoming the Explainability and Interpretability Challenges in Large Language Models (LLMs)

    Oct 15 2024 ,  By: Fenil Dholariya

    As AI and LLMs reshape industries, understanding their decision-making is essential. This blog highlights key challenges in AI explainability and interpretability and explores techniques like attention mechanisms and model distillation. Discover how explainable AI fosters trust, compliance, and accountability in today’s technology.

    Read More

  • Accelerate the development of AI applications using low-code and no-code tools

    Apr 11 2023

    Low-code platforms are typically geared towards technologists and offer ways to build and maintain software with minimal or no coding effort. Low-code and no-code platforms enable the creation of a variety of applications, websites, mobile apps, forms, dashboards, data pipelines, and integrations.

    Read More

Blog Categroy