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When ‘Chatbot’ Is a Dirty Word: 3 Misconceptions Business Leaders Have About Conversational AI

Unite.AI

Today’s “chatbots,” on the other hand, are more frequently referring to conversational AI, a tool with much broader capabilities and use cases. And because we now find ourselves in the midst of the generative AI hype cycle, all three of these terms are being used interchangeably.

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Generative AI use cases for the enterprise

IBM Journey to AI blog

The quality of outputs depends heavily on training data, adjusting the model’s parameters and prompt engineering, so responsible data sourcing and bias mitigation are crucial. Imagine training a generative AI model on a dataset of only romance novels.

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Chatgpt-4 v/s Google Bard: A Head-to-Head Comparison

Analytics Vidhya

Introduction As artificial intelligence and machine learning continue to evolve at a rapid pace, we find ourselves in a world where chatbots are becoming increasingly commonplace. Google recently made headlines with the release of Bard, its language model for dialogue applications (LaMDA).

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Conversational AI with LangChain and Comet

Heartbeat

AI Chatbots offer 24/7 availability support, minimize errors, save costs, boost sales, and engage customers effectively. Businesses are drawn to chatbots not only for the aforementioned reasons but also due to their user-friendly creation process. This evolution paved the way for the development of conversational AI.

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How Can You Showcase AI Skills To Supercharge Your Resume?

Flipboard

Many use AI chatbots as nothing more than search engines — but with enough know-how, you can have these impressive LLMs write complicated code, debug previously written code, write copy, write music, and more. You’ll need to tailor this section based on your career field, experiences and various skills.

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How to Improve the Reliability of ChatGPT: Techniques and Tips

Analytics Vidhya

Large language models (LLM) such as GPT-4 have significantly progressed in natural language processing and generation. These models are capable of generating high-quality text with remarkable fluency and coherence. However, they often fail when tasked with complex operations or logical reasoning.

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LangChain’s String Evaluators: How to Assess Language Model Output

Heartbeat

An In-depth Look into Evaluating AI Outputs, Custom Criteria, and the Integration of Constitutional Principles Photo by Markus Winkler on Unsplash Introduction In the age of conversational AI, chatbots, and advanced natural language processing, the need for systematic evaluation of language models has never been more pronounced.

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