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Hallucinating Reality. An Essay on Business Benefits of Accurate LLMs and LLM Hallucination Reduction Methods

deepsense.ai

The Truth Is Out There So, how to reduce hallucinations in LLMs? What are the techniques for minimizing LLM hallucinations? Design systems that support accurate LLM performance – use grounding to anchor outputs of a language model to a trusted source. Here are a few approaches.

LLM 52
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Injecting Domain Expertise Into Your AI System

Topbots

If these nuances arent accounted for, the AI might learn an overly simplified view of supply chain dynamics, resulting in misleading risk assessments and poor recommendations. AI models work with what they have, assuming that all key factors are already present. Consider an AI model built to predict supplier reliability.

LLM 52
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Building AI Products With A Holistic Mental Model

Topbots

The model serves as a tool for the discussion, planning, and definition of AI products by cross-disciplinary AI and product teams, as well as for alignment with the business department. It aims to bring together the perspectives of product managers, UX designers, data scientists, engineers, and other team members.

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Accelerate video Q&A workflows using Amazon Bedrock Knowledge Bases, Amazon Transcribe, and thoughtful UX design

AWS Machine Learning Blog

Not only are large language models (LLMs) capable of answering a users question based on the transcript of the file, they are also capable of identifying the timestamp (or timestamps) of the transcript during which the answer was discussed. The file is sent to Amazon Transcribe and the resulting transcript is stored in Amazon S3.