Abstract: The embodiments of the present disclosure herein address unresolved problems of predicting future fund rate in the next meeting of a financial regulatory body responsible for regulation of interest rate based on current economic conditions and data of last meeting happened. Embodiments herein provide a method and system for predicting change in a future fund rate by a financial regulatory body responsible for regulation of interest rates. Herein, textual data as well as numerical data are collected to extract useful textual summary of forward-looking statements from large corpus of text data using a pre-trained Large Language Model (LLM) which will contribute to predicting future fund rates. A domain insight matrix is used as a comprehensive framework for guiding the pre-trained LLM on how to approach a task and validate the outputs based on predefined categories and parameters set by domain experts. With prompt optimization efforts, a good quality summary of forward-looking statements is achieved.

Abstract: Human-understandable explanations of Artificial Intelligence (AI) based models are crucial to building transparency and trust in AI based solutions. More importantly, these explanations need to be contextual, applicable to the domain the model is used in and relevant to the concerned stakeholder. Conventionally, there is a lack of communicating these explanations to various stakeholders in a language that they can understand and relate to. The present disclosure facilitates the conversational agents (chat bots) with intelligence and actions that would help them communicate the right information to the right stakeholder in the right way. In the present disclosure, contextual explanation for user queries is generated based on the output from AI models. Here, the impacting features are obtained from the explainer model associated with the prediction model and the contextual information is generated. Further, the contextual information is converted to the contextual explanation to the user.

Abstract: A method and system for dynamic guardrails framework with plug-in functionality for Large Language Model (LLM) application is disclosed. The user requirements stating validations, validation preferences and threshold, and actions on these validations received via configuration file are used to select experts using pretrained LLMs. A wrapper comprising the basic guard rail code based on config file is generated and then optimized over iterative process using prompt optimization for guardrail code generation. The prompt optimizer is configured to generate updated prompt by analyzing the reason for failure or earlier created wrapper against the checks. The guardrail framework comprises a group of infinite tools with pretrained LLMs for specific tasks. The LLM based expert selection in accordance the configuration file enables only required experts to be used. The deliverable guardrail code is a plug-in to be inserted into an LLM application treated as Blackbox without interfering with user prompt.

Abstract: Business rules are currently not documented and are present only as knowledge with subject matter experts (SMEs). The knowledge can be lost with time if it is not extracted or recorded. Existing techniques are unable to extract tacit knowledge and to retain the domain flavor in extracted information. Present disclosure provides a method and a system for extracting tacit knowledge from historical data. The system represents each point in historical data as a large dimensional hyperspace which contains all unstructured information where tacit knowledge can exist. Then, system maps large dimensional hyperspace to smaller dimensional hyperspace using pre-trained large language model (LLM). Thereafter, system, based on the series of downstream tasks, generates a feedback loop to optimally compute dimension of the smaller dimensional hyperspace.

Abstract: Human-understandable explanations of Artificial Intelligence (AI) based models are crucial to building transparency and trust in AI based solutions. More importantly, these explanations need to be contextual, applicable to the domain the model is used in and relevant to the concerned stakeholder. Conventionally, there is a lack of communicating these explanations to various stakeholders in a language that they can understand and relate to. The present disclosure facilitates the conversational agents (chat bots) with intelligence and actions that would help them communicate the right information to the right stakeholder in the right way. In the present disclosure, contextual explanation for user queries is generated based on the output from AI models. Here, the impacting features are obtained from the explainer model associated with the prediction model and the contextual information is generated. Further, the contextual information is converted to the contextual explanation to the user.

Abstract: Disclosed is an edible composition for enhanced immunity comprising theanine or a source of theanine and a herb selected from Shankhpushpi, Shatavari, or a mixture thereof.

Abstract: A composition comprising tea, 0.1 to 15% by weight of herb selected from Shankhpushpi, Shatavari, Vidarikhand, Arogyapacha or a mixture thereof; and 0.01 to 0.5% by weight of a flavouring agent is disclosed. Also disclosed is a process for manufacturing the composition.