Notes on operations, automation, and AI for regulated firms — written by the team at MoiraCorp.
GenAI can adopt a persona and “make decisions” or “behave” in a way that can be quantified. This technique is used to simulate scenarios, which can then be analyzed quantitatively and used in particular to assess multi-criteria decision alternatives
Extracting quantitative information using GenAI tools requires to properly structure the prompts used to question them to efficiently use their large language models (LLMs)
Machine learning is a type of artificial intelligence that enables computers to learn from existing knowledge and experiment results. These models are traditionally used for prediction and can be augmented by GenAI for training data generation and screening in particular
Retrieval Augmented Generation (RAG) is a critical technique using proprietary or domain specific documents to augment base LLMs to address specific enterprise or applications needs.
The use of AI agents offers a significant leap forward from traditional task automation by infusing intelligence, adaptability, and autonomy into critical IT processes.
GenAI can “read” text and assign numerical values or categories that can then be counted or classified. When appropriately queried, the values can be turned into probabilities of assertion.
Prompt engineering and RAG can be used cooperatively to process knowledge represented through expert oriented rules (if-then statements) and enable deductive reasoning leading to enhanced decision making
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GenAI can adopt a persona and “make decisions” or “behave” in a way that can be quantified. This technique is used to simulate scenarios, which can then be analyzed quantitatively and used in particular to assess multi-criteria decision alternatives
Extracting quantitative information using GenAI tools requires to properly structure the prompts used to question them to efficiently use their large language models (LLMs)
Machine learning is a type of artificial intelligence that enables computers to learn from existing knowledge and experiment results. These models are traditionally used for prediction and can be augmented by GenAI for training data generation and screening in particular
Retrieval Augmented Generation (RAG) is a critical technique using proprietary or domain specific documents to augment base LLMs to address specific enterprise or applications needs.
The use of AI agents offers a significant leap forward from traditional task automation by infusing intelligence, adaptability, and autonomy into critical IT processes.
GenAI can “read” text and assign numerical values or categories that can then be counted or classified. When appropriately queried, the values can be turned into probabilities of assertion.
Prompt engineering and RAG can be used cooperatively to process knowledge represented through expert oriented rules (if-then statements) and enable deductive reasoning leading to enhanced decision making
