Enterprises are adopting Generative AI to help solve many complex use cases with natural language instructions. Building a Gen AI application involves multiple components such as an LLM, data sources, vector store, prompt engineering, and RAG. GenAIOps defines operational best practices for the holistic management of DataOps (Data Operations), LLMOps (Large Language Model Life cycle management), and DevOps (Development and Operations) for building, testing, and deploying generative AI applications.
While pilot projects using Generative AI can start effortlessly, most enterprises need help progressing beyond this phase. According to Everest Research, a staggering 50%+ projects do not move beyond the pilots as they face hurdles due to the absence of established GenAIOps practices. Each step presents unique challenges, from connecting to enterprise data to navigating the complexities of embedding algorithms and managing query phases. These include:
Effective GenAIOps operationalization requires skills such as AI engineers, safety and security experts, and domain experts. The diagram below provides best practices for a typical RAG workflow depicted in the challenges section. Let's dive into the best practices below,
Experts in AI engineering, cloud computing, security, data engineering, and UX engineering built Karini’s Generative AI platform. The combined expertise and platform design provide built-in GenAIOps best practices. These best practices enable enterprises to execute rapid prototyping, production deployment, and continuous monitoring. The Generative AI application's observability capabilities, evaluation, and central performance monitoring allow continuous quality and enterprise governance improvement.
Staying at the forefront of scientific advancement and the evolving landscape of models, Karini AI eliminates technical debt. Our no-code approach to Generative AI application deployment ensures you don’t compromise on quality or speed in bringing products to market. Karini AI is adaptable and perfect for various applications, including virtual assistants, text generation, summarization, Q&A, semantic search, classification, and image creation.
About the Author
Deepali Rajale is a founder of Karini AI with a mission to democratize generative AI across enterprises. She enjoys blogging about Generative AI, coaching customers to optimize Generative AI practice. She loves to spend time outdoors camping with her family and also a poet and has published a book.
Karini AI: Building Better AI, Faster.
Orchestrating GenAI Apps for Enterprises GenAiOps at scale.