Retrieval-augmented generation (RAG) is a hybrid AI approach that combines retrieval-based methods with generative models to improve the quality and accuracy of generated content. This approach benefits tasks requiring factual accuracy and natural language generation, such as question-answering, summarization, or generating content based on specific knowledge. How RAG Works: RAG integrates two core components: Retrieval… Continue reading Retrieval-Augmented Generation (RAG)
Tag: ArtificialIntelligence
Cybersecurity Risks in AI Lifecycle
Aligning AI risks with LLMOps stages involves identifying where specific risks are most likely to arise and ensuring that each phase has appropriate controls to mitigate these risks. AI risks can indeed occur in multiple stages or phases, as many risks are pervasive and can impact different aspects of the AI lifecycle. 1. Model Development… Continue reading Cybersecurity Risks in AI Lifecycle
AI Cybersecurity Risks & Controls
Cybersecurity risks have become increasingly prominent in AI. Some of them are data poisoning, personal and confidential information in data, prompt injection, lack of data transparency, unreliable source attribution, and unexplainable outputs. These issues can compromise the integrity, security, and reliability of AI systems. Data Poisoning 1. Threat: Data Poisoning in AI Description: Data poisoning… Continue reading AI Cybersecurity Risks & Controls
AI, Generative AI and LLMOps
Artificial Intelligence (AI) is a field of computer science and technology that aims to enable computers and machines to simulate human learning, comprehension, problem-solving, decision-making, creativity, and autonomy. It involves systems’ ability to make decisions, process vast amounts of data, and adapt over time based on the information they receive. AI can range from simple… Continue reading AI, Generative AI and LLMOps