#retrieval augmented generation

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#generative-ai

NVIDIA GTC 2024: Top 5 Trends

NVIDIA GPUs power generative AI for enterprise
Trends at NVIDIA GTC 2024: retrieval-augmented generation and 'AI factories'

Google's DataGemma is the first large-scale Gen AI with RAG - why it matters

Google's DataGemma enhances generative AI's accuracy by integrating retrieval-augmented generation with publicly available data from Data Commons.

Want generative AI LLMs integrated with your business data? You need RAG

RAG integrates LLMs with information retrieval, enhancing AI's accuracy and relevance in business applications.

Understanding RAG: How to integrate generative AI LLMs with your business knowledge

RAG integrates generative AI with information retrieval, enhancing accuracy and relevance in business applications.

The Popular Way to Build Trusted Generative AI? RAG - SPONSOR CONTENT FROM AWS

To build trust in generative AI, organizations must customize large language models to ensure accuracy and relevance.

DataStax CTO Discusses RAG's Role in Reducing AI Hallucinations

RAG is essential for integrating generative AI with enterprise-specific data to enhance accuracy in outputs.

NVIDIA GTC 2024: Top 5 Trends

NVIDIA GPUs power generative AI for enterprise
Trends at NVIDIA GTC 2024: retrieval-augmented generation and 'AI factories'

Google's DataGemma is the first large-scale Gen AI with RAG - why it matters

Google's DataGemma enhances generative AI's accuracy by integrating retrieval-augmented generation with publicly available data from Data Commons.

Want generative AI LLMs integrated with your business data? You need RAG

RAG integrates LLMs with information retrieval, enhancing AI's accuracy and relevance in business applications.

Understanding RAG: How to integrate generative AI LLMs with your business knowledge

RAG integrates generative AI with information retrieval, enhancing accuracy and relevance in business applications.

The Popular Way to Build Trusted Generative AI? RAG - SPONSOR CONTENT FROM AWS

To build trust in generative AI, organizations must customize large language models to ensure accuracy and relevance.

DataStax CTO Discusses RAG's Role in Reducing AI Hallucinations

RAG is essential for integrating generative AI with enterprise-specific data to enhance accuracy in outputs.
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#retrieval-augmented-generation

Why experts are using the word 'bullshit' to describe AI's flaws

AI language models can produce false outputs, termed as 'hallucinations' or 'bullshit', with retrieval-augmented generation technology attempting to reduce such errors.

BMW showed off hallucination-free AI at CES 2024

AI was a major trend at CES 2024, with car manufacturers like BMW, Mercedes-Benz, and Volkswagen embracing the technology.
BMW's implementation of AI in cars focuses on Retrieval-Augmented Generation, allowing the AI to provide accurate information from internal BMW documentation about the car.

Why are Google's AI Overviews results so bad?

AI Overviews' unreliable responses point to the challenges of AI systems, prompting the need for continuous improvement and stricter content filtering.

Voyage AI is building RAG tools to make AI hallucinate less | TechCrunch

AI inaccuracies can significantly impact businesses, raising concerns among employees about the reliability of generative AI systems.
Voyage AI utilizes RAG systems to enhance the reliability of AI-generated information, addressing the critical challenge of AI hallucinations.

RAG-Powered Copilot Saves Uber 13,000 Engineering Hours

Uber's Genie AI co-pilot improves on-call support efficiency, using RAG to provide real-time, accurate responses and save engineering hours.

Virtual Panel: What to Consider when Adopting Large Language Models

API solutions offer speed for iteration; self-hosted models may provide better cost and privacy benefits long-term.
Prompt engineering and RAG should be prioritized before model fine-tuning.
Smaller open models can be effective alternatives to large closed models for many tasks.
Mitigating hallucinations in LLMs can be accomplished using trustworthy sources with RAG.
Employee education on LLMs' capabilities and limitations is essential for successful adoption.

Why experts are using the word 'bullshit' to describe AI's flaws

AI language models can produce false outputs, termed as 'hallucinations' or 'bullshit', with retrieval-augmented generation technology attempting to reduce such errors.

BMW showed off hallucination-free AI at CES 2024

AI was a major trend at CES 2024, with car manufacturers like BMW, Mercedes-Benz, and Volkswagen embracing the technology.
BMW's implementation of AI in cars focuses on Retrieval-Augmented Generation, allowing the AI to provide accurate information from internal BMW documentation about the car.

Why are Google's AI Overviews results so bad?

AI Overviews' unreliable responses point to the challenges of AI systems, prompting the need for continuous improvement and stricter content filtering.

Voyage AI is building RAG tools to make AI hallucinate less | TechCrunch

AI inaccuracies can significantly impact businesses, raising concerns among employees about the reliability of generative AI systems.
Voyage AI utilizes RAG systems to enhance the reliability of AI-generated information, addressing the critical challenge of AI hallucinations.

RAG-Powered Copilot Saves Uber 13,000 Engineering Hours

Uber's Genie AI co-pilot improves on-call support efficiency, using RAG to provide real-time, accurate responses and save engineering hours.

Virtual Panel: What to Consider when Adopting Large Language Models

API solutions offer speed for iteration; self-hosted models may provide better cost and privacy benefits long-term.
Prompt engineering and RAG should be prioritized before model fine-tuning.
Smaller open models can be effective alternatives to large closed models for many tasks.
Mitigating hallucinations in LLMs can be accomplished using trustworthy sources with RAG.
Employee education on LLMs' capabilities and limitations is essential for successful adoption.
moreretrieval-augmented-generation

Improving ChatGPT's Ability to Understand Ambiguous Prompts

Large language models (LLMs) like ChatGPT are driving innovative research and applications.
Retrieval augmented generation (RAG) enhances the accuracy of generated responses by integrating external knowledge.
The open source project Akcio utilizes the RAG approach to create a robust question-answer system.
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