#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.
moregenerative-ai
#artificial-intelligence

Microsoft .NET Conf: Focus on AI

The .NET Conf: Focus series 2024 showcased AI development, providing in-depth sessions for developers to effectively leverage AI in .NET applications.

Deep Learning Architecture: Naive Retrieval-Augmented Generation(RAG)

RAG systems enhance LLMs by efficiently retrieving and integrating relevant data, addressing their limitations in processing recent information.

LightRAG - Is It a Simple and Efficient Rival to GraphRAG? | HackerNoon

LightRAG enhances RAG systems by offering efficient retrieval and seamless updates, surpassing traditional methods like GraphRAG.

Deep Learning Architecture: Naive Retrieval-Augmented Generation(RAG)

Naive RAG simplifies data retrieval and generation processes through indexing, retrieving, and generating, optimizing response accuracy for user queries.

Microsoft .NET Conf: Focus on AI

The .NET Conf: Focus series 2024 showcased AI development, providing in-depth sessions for developers to effectively leverage AI in .NET applications.

Deep Learning Architecture: Naive Retrieval-Augmented Generation(RAG)

RAG systems enhance LLMs by efficiently retrieving and integrating relevant data, addressing their limitations in processing recent information.

LightRAG - Is It a Simple and Efficient Rival to GraphRAG? | HackerNoon

LightRAG enhances RAG systems by offering efficient retrieval and seamless updates, surpassing traditional methods like GraphRAG.

Deep Learning Architecture: Naive Retrieval-Augmented Generation(RAG)

Naive RAG simplifies data retrieval and generation processes through indexing, retrieving, and generating, optimizing response accuracy for user queries.
moreartificial-intelligence

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.

Enhancing RAG with Knowledge Graphs: Integrating Llama 3.1, NVIDIA NIM, and LangChain for Dynamic AI | HackerNoon

Dynamic query generation enhances retrieval from knowledge graphs over relying solely on LLMs, ensuring consistency and control in query formulation.

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.
#large-language-models

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.

5 LLM-based Apps for Developers | HackerNoon

LLMs significantly enhance developer productivity by automating tasks and providing access to updated information.

Comprehensive Tutorial on Building a RAG Application Using LangChain | HackerNoon

RAG uses context from private data to enhance language model responses, addressing information gaps.
RAG systems can revolutionize enterprise applications of AI by accessing specific, relevant information.

Your Guide to Starting With RAG for LLM-Powered Applications

Retrieval augmented generation (RAG) is an ideal starting point for designing enterprise large language models (LLMs).
Start simple and gradually build complexity when developing LLM-powered applications.

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.

5 LLM-based Apps for Developers | HackerNoon

LLMs significantly enhance developer productivity by automating tasks and providing access to updated information.

Comprehensive Tutorial on Building a RAG Application Using LangChain | HackerNoon

RAG uses context from private data to enhance language model responses, addressing information gaps.
RAG systems can revolutionize enterprise applications of AI by accessing specific, relevant information.

Your Guide to Starting With RAG for LLM-Powered Applications

Retrieval augmented generation (RAG) is an ideal starting point for designing enterprise large language models (LLMs).
Start simple and gradually build complexity when developing LLM-powered applications.
morelarge-language-models

How to Turn Your OpenAPI Specification Into an AI Chatbot With RAG | HackerNoon

Startups struggle with API documentation due to lack of time, but automated tools can ease this burden.
Combining OpenAPI with RAG can significantly streamline documentation accessibility.
Retrieval Augmented Generation can improve the quality and accuracy of responses in API-related queries.

AI Embeddings explained in depth

AI embeddings enhance search accuracy by understanding context and user intent.
Traditional search engines often yield irrelevant results due to lack of nuance.
Ollama API provides tools for efficient embedding creation and use.

Anyword Wants To Be The AI Marketing Tool In Charge Of All Other AI Marketing Tools | AdExchanger

Anyword offers a platform that scores and analyzes content from various AI tools used by marketers, leveraging retrieval augmented generation.

Using LlamaIndex to add personal data to LLMs - LogRocket Blog

RAG integrates retrieval mechanisms with LLMs for contextual text generation.

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.

Council Post: What's The RAGs? How To Unlock Explosive Marketing Success With AI

RAG enhances language models with retrieval-augmented technology for personalized content creation in advertising and digital marketing.

NVIDIA Launches RTX, Personalized GPT Models

Users can create personalized chatbots using NVIDIA's RTX technology and TensorTR-LLM.
System requirements for running RTX include an RTX 2080 Ti or better graphics card, 32GB of RAM, and 10GB of free disk space.

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.
from thenewstack.io
11 months ago

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.

Amazon proposes a new AI benchmark to measure RAG

Generative artificial intelligence (GenAI) is expected to soar in enterprises through methodologies like retrieval-augmented generation (RAG), accompanied by challenges.

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.
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