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Challenge: Often, prompt authors create numerous versions of a prompt for one task during the experimentation, which can become overwhelming. A significant challenge during this process is tracking the different prompt versions you're testing and the ability to manage and incorporate them into your Gen AI workflow. Prompt Engineering for complex use cases such as Legal, Financial Advisor, HR advisor applications, etc., requires a lot of experimentation to ensure accuracy, quality, and safety guardrails. Although many prompt playgrounds exist, managing the prompt history comparison of large sets of experiments is still done offline using spreadsheets and entirely decoupled from Gen AI workflows, removing prompt lineage. Prompt Engineering with Karini’s Prompt Playground: Karini AI ’s prompt playground revolutionizes how prompts are created, tested, and perfected across their lifecycle. This user-friendly and dynamic platform transforms domain experts into skilled prompt masters, off

  In an era where data is the new gold, businesses have grappled with the challenge of data silos - isolated reservoirs of information accessible only to specific organizational factions. This compartmentalization of data is the antithesis of what we term 'healthy' data: information that's universally comprehensible and accessible, fueling informed decision-making across an enterprise. For decades, enterprises have endeavored to dismantle these silos, only to inadvertently erect new ones dictated by the need for efficient data flows and technological limitations. However, the landscape is radically transforming, thanks to Generative AI (Gen AI) and its groundbreaking capabilities. The Transformational Shift with Gen AI: The advent of Gen AI heralds an unprecedented shift in data management and accessibility. With the advent of Retrieval Augmented Generation (RAG) and its integration into infinitely expandable vector data stores, the once-unthinkable is now a tangible realit

  In the past twelve months, the corporate landscape has been abuzz with the potential of generative AI as a groundbreaking innovation. Despite broad recognition of its transformative power, many firms have adopted a tentative stance, cautiously navigating the implementation of this technology. Is a cautious approach prudent, or does it inadvertently place companies at risk of lagging in a rapidly evolving technological landscape? Recent investigations forecast the staggering benefits of  generative AI , suggesting potential productivity gains in trillions of dollars per annum by 2030 if harnessed effectively. The rewards surpass the apprehensions, provided the adoption of this technology is executed with strategic foresight. It's not about restricting generative AI but about sculpting its usage within well-defined parameters to mitigate potential challenges, including uncontrolled expenses, security breaches, compliance issues, and employee engagement. Below, we outline ten strate

  Karini AI is proud to announce a new feature to help businesses enhance user experiences by replacing the everlasting ellipsis with streaming responses. Following the latest trends in open-source generative AI, this leap forward for purpose built enterprise-focused AI using natural language questions and answers is evolving copilot to the next level. Today, ChatGPT has set the tone and standard for the copilot user experience as it gives us a conversational impression due to the streaming tokens appearing on the screen. Creating the streaming experience with large models like GPT4 is more straightforward but, at the same time, challenging in enterprise environments where you may need a wide array of state-of-the-art (SOTA) model providers ranging from open source to SaaS. Each LLM is nuanced in its capabilities to stream tokens and the quality of response. So, it’s tough to build a uniform user experience across the ecosystem of model providers, diminishing and degrading users’ engag