#retrieval-augmented-generation
#retrieval-augmented-generation

2 years ago

Artificial intelligence

GPT-4.1's 1M-token Context Window is Impressive but Insufficient for Real-world Use Cases | HackerNoon

Large context windows in AI are attractive but come with hidden costs and complexities that impact their practical usability.

fromDATAVERSITY

Business intelligence

Oct 9 AArch Webinar: From Pre-Trained to Fine-Tuned - How to Get the Most Out of Vector, RAG, and Small Language Models - DATAVERSITY

This webinar offers insights into adapting AI models for enterprise needs, focusing on fine-tuning and efficiency.

2 years ago

Artificial intelligence

GPT-4.1's 1M-token Context Window is Impressive but Insufficient for Real-world Use Cases | HackerNoon

Large context windows in AI are attractive but come with hidden costs and complexities that impact their practical usability.

fromDATAVERSITY

Business intelligence

Oct 9 AArch Webinar: From Pre-Trained to Fine-Tuned - How to Get the Most Out of Vector, RAG, and Small Language Models - DATAVERSITY

This webinar offers insights into adapting AI models for enterprise needs, focusing on fine-tuning and efficiency.

more#ai-models

#large-language-models

LLM + RAG: Creating an AI-Powered File Reader Assistant

AI simplifies daily tasks, enhancing productivity through tools like chatbots and LLMs.

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.

5 months ago

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.

fromArs Technica

Why AI language models choke on too much text

Large language models are evolving to handle more tokens, allowing for greater complexity in tasks and improved capabilities.

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.

Artificial intelligence

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.

LLM + RAG: Creating an AI-Powered File Reader Assistant

AI simplifies daily tasks, enhancing productivity through tools like chatbots and LLMs.

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.

5 months ago

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.

fromArs Technica

Why AI language models choke on too much text

Large language models are evolving to handle more tokens, allowing for greater complexity in tasks and improved capabilities.

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.

more#large-language-models

Artificial intelligence

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.

#generative-ai

fromClickUp

RAG Use Cases: Enhance AI, ML Workflows Efficiently | ClickUp

Retrieval-augmented generation (RAG) enhances AI accuracy by integrating dynamic external data with traditional models.

fromAbove the Law

Deep Research - Above the Law

The main challenge of Generative AI like ChatGPT is hallucination, particularly in critical fields like law.

fromInfoWorld

3 months ago

The bitter lesson for generative AI adoption

Relying on retrieval-augmented generation and prompt engineering is a more sustainable strategy compared to constant model training and fine-tuning.

fromFast Company

4 weeks ago

Artificial intelligence

How Hebbia is building AI for in-depth research

Hebbia's AI addresses limitations in generative models by improving retrieval methods for accurate data responses.

Miscellaneous

Synthetic Data Generation with LLMs

Generative AI use cases, particularly Retrieval-Augmented Generation (RAG), are crucial for balancing implementation ease with real-world impact in financial firms.

fromClickUp

RAG Use Cases: Enhance AI, ML Workflows Efficiently | ClickUp

Retrieval-augmented generation (RAG) enhances AI accuracy by integrating dynamic external data with traditional models.

fromAbove the Law

Deep Research - Above the Law

The main challenge of Generative AI like ChatGPT is hallucination, particularly in critical fields like law.

fromInfoWorld

3 months ago

The bitter lesson for generative AI adoption

Relying on retrieval-augmented generation and prompt engineering is a more sustainable strategy compared to constant model training and fine-tuning.

fromFast Company

4 weeks ago

Artificial intelligence

How Hebbia is building AI for in-depth research

Hebbia's AI addresses limitations in generative models by improving retrieval methods for accurate data responses.

Miscellaneous

Synthetic Data Generation with LLMs

Generative AI use cases, particularly Retrieval-Augmented Generation (RAG), are crucial for balancing implementation ease with real-world impact in financial firms.

more#generative-ai

#machine-learning

Productivity

Enhancing RAG: Beyond Vanilla Approaches

RAG enhances language models but struggles in complex retrieval scenarios.

fromTheregister

Training AI Is tough; Deploying in enterprise is next-level

Fine tuning is not a magic solution for AI; RAG might be a better approach for integrating LLMs effectively.

Retrieval Augmented Generation in SQLite

SQLite simplifies the approach to retrieval-augmented generation (RAG) without the need for complex frameworks or cloud databases.

Microsoft Introduces CoRAG: Enhancing AI Retrieval with Iterative Reasoning

CoRAG enhances Retrieval-Augmented Generation by enabling dynamic, iterative retrieval processes for more accurate answers.

Artificial intelligence

Smart Queries, Smarter Answers: Adaptive-RAG in Action | HackerNoon

Adaptive-RAG dynamically adjusts query strategies based on complexity, enhancing accuracy and efficiency in QA systems.

Artificial intelligence

Adaptive Ascension: LLMs, Efficiency, and Query Complexity | HackerNoon

Adaptive-RAG method surpasses traditional strategies by tailoring responses according to query complexity in retrieval-augmented generation systems.

Productivity

Enhancing RAG: Beyond Vanilla Approaches

RAG enhances language models but struggles in complex retrieval scenarios.

fromTheregister

Training AI Is tough; Deploying in enterprise is next-level

Fine tuning is not a magic solution for AI; RAG might be a better approach for integrating LLMs effectively.

Retrieval Augmented Generation in SQLite

SQLite simplifies the approach to retrieval-augmented generation (RAG) without the need for complex frameworks or cloud databases.

Microsoft Introduces CoRAG: Enhancing AI Retrieval with Iterative Reasoning

CoRAG enhances Retrieval-Augmented Generation by enabling dynamic, iterative retrieval processes for more accurate answers.

Artificial intelligence

Smart Queries, Smarter Answers: Adaptive-RAG in Action | HackerNoon

Adaptive-RAG dynamically adjusts query strategies based on complexity, enhancing accuracy and efficiency in QA systems.

Artificial intelligence

Adaptive Ascension: LLMs, Efficiency, and Query Complexity | HackerNoon

Adaptive-RAG method surpasses traditional strategies by tailoring responses according to query complexity in retrieval-augmented generation systems.

more#machine-learning

#artificial-intelligence

fromMedium

Key Takeaways from the AI Builders Summit: A Four-Week Deep Dive into AI Development

The AI Builders Summit highlighted advancements in AI technologies across various domains, emphasizing practical strategies for building and optimizing AI models.

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.

1 year ago

Miscellaneous

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.

beer

Can Adaptive QA Frameworks Solve the Query Complexity Puzzle? | HackerNoon

Adaptive Retrieval-Augmented Generation improves response accuracy by dynamically addressing the complexity of user queries in question-answering tasks.

fromMedium

Key Takeaways from the AI Builders Summit: A Four-Week Deep Dive into AI Development

The AI Builders Summit highlighted advancements in AI technologies across various domains, emphasizing practical strategies for building and optimizing AI models.

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.

1 year ago

Miscellaneous

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.

more#artificial-intelligence

beer

Can Adaptive QA Frameworks Solve the Query Complexity Puzzle? | HackerNoon

Adaptive Retrieval-Augmented Generation improves response accuracy by dynamically addressing the complexity of user queries in question-answering tasks.

3 years ago

Roam Research

Build a Fully Local RAG System with rlama and Ollama-No Cloud, No Dependencies | HackerNoon

RAG enhances LLM responses by retrieving relevant document snippets, and rlama enables a fully local, offline implementation for data privacy.

#ai

fromUX Magazine

Is RAG the Future of Knowledge Management?

RAG enhances LLMs by connecting them to external databases for real-time accuracy.

fromHoloViz Blog

Business intelligence

Lumen AI Announcement - HoloViz Blog

Lumen AI empowers users to conduct complex data analyses through a fully open-source framework.

Business 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.

fromUX Magazine

Is RAG the Future of Knowledge Management?

RAG enhances LLMs by connecting them to external databases for real-time accuracy.

fromHoloViz Blog

Business intelligence

Lumen AI Announcement - HoloViz Blog

Lumen AI empowers users to conduct complex data analyses through a fully open-source framework.

Business 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.

more#ai

1 year ago

JavaScript

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.

fromGeeky Gadgets

Artificial intelligence

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.

fromAdExchanger

8 months ago

Artificial intelligence

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.

fromMIT Technology Review

10 months ago

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.

fromForbes

11 months ago

Marketing

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.

9 months ago

Artificial intelligence

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.