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K2view Finds that Just 2% of U.S. and UK Businesses are Ready for GenAI Deployment
K2view Named a Visionary in the 2023 Gartner MQ for Data Integration Tools
K2view Launches New Synthetic Data Management Solution
K2view a Leader in the 2023 SPARK Matrix for Data Masking Tools

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AI Data Fusion – New from K2view

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AI Data Fusion – New from K2view

AI Data Fusion – New from K2view

AI Data Fusion injects enterprise data into Large Language Models – on demand and in real time – to ground GenAI apps and deliver responses users trust.

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RAG
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AI Data Privacy: Protecting Financial Information in the AI Era

November 14, 2024

AI Data Privacy: Protecting Financial Information in the AI Era

AI data privacy is the set of security measures taken to protect the sensitive data collected, stored, and processed by AI apps, frameworks, and models.

RAG

LLM Agent Architecture Enhances GenAI Task Management

November 10, 2024

LLM Agent Architecture Enhances GenAI Task Management

An LLM agent architecture is a framework combining a large language model with other components to enable better task execution and real-world interaction.

RAG

Generative AI Data Augmentation: An IDC Research Snapshot

November 4, 2024

Generative AI Data Augmentation: An IDC Research Snapshot

GenAI data augmentation enhances AI models with structured, unstructured, and semi-structured data from enterprise systems for improved query responses.

RAG

LLM Agent Framework: Quietly Completing Complex AI Tasks

October 31, 2024

LLM Agent Framework: Quietly Completing Complex AI Tasks

An LLM agent framework is a software platform that creates and manages LLM-based agents that autonomously interact with their environment to fulfill tasks.

RAG

Prompt Engineering vs Fine-Tuning: Understanding the Pros and Cons

October 29, 2024

Prompt Engineering vs Fine-Tuning: Understanding the Pros and Cons

Prompt engineering is a process that improves LLM responses by well crafted inputs. Fine-tuning trains a model on domain-specific data. Which to use when?

RAG

LLM Function Calling Goes Way Beyond Text Generation

October 27, 2024

LLM Function Calling Goes Way Beyond Text Generation

LLM function calling is the ability of a large language model to perform actions besides generating text by invoking APIs to interface with external tools.

RAG

RAG Architecture + LLM Agent = Better Responses

October 20, 2024

RAG Architecture + LLM Agent = Better Responses

RAG architectures powered by LLM agents retrieve relevant data from internal and external sources to generate more accurate and contextual responses.

RAG

AI Data Governance Spotlights Privacy and Quality

October 14, 2024

AI Data Governance Spotlights Privacy and Quality

The emergence of AI brings data governance into sharp focus, because grounding LLMs with secure, trusted data is the only way to ensure accurate responses.

RAG

What are LLM agents?

October 7, 2024

What are LLM agents?

LLM agents are AI tools that leverage Large Language Models (LLMs) to perform tasks, make decisions, and interact with users or other systems autonomously.

RAG

LLM Guardrails Guide AI Toward Safe, Reliable Outputs

September 25, 2024

LLM Guardrails Guide AI Toward Safe, Reliable Outputs

LLM guardrails are agents that ensure that your model generates safe, accurate, and ethical responses by monitoring and controlling its inputs and outputs.

RAG

Generative AI Use Cases: Top 10 for Enterprises in 2025

September 17, 2024

Generative AI Use Cases: Top 10 for Enterprises in 2025

Generative AI use cases are AI-powered workloads designed to create content, enhance creativity, automate tasks, and personalize user experiences.

RAG

LLM Vector Database: Why it’s Not Enough for RAG

September 16, 2024

LLM Vector Database: Why it’s Not Enough for RAG

LLM vector databases store vector embeddings for similarity search, but lack the structural data integration and contextual reasoning needed for RAG.

RAG

Prompt Engineering Techniques: Top 5 for 2025

September 11, 2024

Prompt Engineering Techniques: Top 5 for 2025

Prompt engineering techniques are methods that enhance the accuracy of LLM responses, including zero-shot, few-shot, chain-of-thought prompting and others.

RAG

LLM Text-to-SQL Solutions: Top Challenges and Tips to Overcoming Them

September 11, 2024

LLM Text-to-SQL Solutions: Top Challenges and Tips to Overcoming Them

LLM-based text-to-SQL is the process of using Large Language Models (LLMs) to automatically convert natural language questions into SQL database queries.

RAG

AI Prompt Engineering: The Art of AI Instruction

September 5, 2024

AI Prompt Engineering: The Art of AI Instruction

AI prompt engineering is the process of giving a Large Language Model (LLM) effective instructions for generating accurate responses to user queries.

RAG

Grounding Data is Like Doing a Reality Check on Your LLM

August 27, 2024

Grounding Data is Like Doing a Reality Check on Your LLM

Grounding data is the process of exposing your Large Language Model (LLM) to real-world data to ensure it responds to queries more accurately and reliably.

RAG

Chain-of-Thought Reasoning Supercharges Enterprise LLMs

August 26, 2024

Chain-of-Thought Reasoning Supercharges Enterprise LLMs

Chain-of-thought reasoning is the process of breaking down complex tasks into simpler steps. Applying it to LLM prompts results in more accurate responses.

RAG

Enterprise LLM: The Challenges and Benefits of Generative AI via RAG

August 22, 2024

Enterprise LLM: The Challenges and Benefits of Generative AI via RAG

Enterprise Large Language Models (LLMs) using Retrieval-Augmented Generation (RAG) enhance the accuracy and context of their responses with generative AI.

RAG

RAG vs Fine-Tuning vs Prompt Engineering: And the Winner is...

August 8, 2024

RAG vs Fine-Tuning vs Prompt Engineering: And the Winner is...

RAG, fine-tuning, and prompt engineering are all techniques designed to enhance LLM response clarity, context, and compliance. Which works best for you?

RAG

Enterprise RAG: Beware of Connecting Your LLM Directly to Your Source Systems!

August 8, 2024

Enterprise RAG: Beware of Connecting Your LLM Directly to Your Source Systems!

When deploying enterprise RAG, you may want to give your LLM’s agents and functions direct access your operational systems. But that’s not a great idea.

RAG

What is an AI Database Schema Generator and Why is it Critical for Your LLM

August 6, 2024

What is an AI Database Schema Generator and Why is it Critical for Your LLM

An AI database schema generator is a tool using AI to automate the creation and management of database schemas. Schema-aware LLMs respond more accurately.

RAG

RAG Prompt Engineering Makes LLMs Super Smart

August 5, 2024

RAG Prompt Engineering Makes LLMs Super Smart

Retrieval-Augmented Generation (RAG) prompt engineering is a generative AI technique that enhances the responses generated by Large Language Models (LLMs).

RAG

AI Data Quality: The Race is On

July 31, 2024

AI Data Quality: The Race is On

The concentration on generative AI puts data quality into sharp focus. Grounding LLMs with trusted private data and knowledge is more essential than ever.

RAG

RAG for Structured Data: The Pros and Cons of Using Data Lakes

July 10, 2024

RAG for Structured Data: The Pros and Cons of Using Data Lakes

Are data lakes and/or warehouses the best platforms for integrating structured data into retrieval-augmented generation architectures? Let’s find out.

RAG

Grounding AI Reduces Hallucinations and Increases Response Accuracy

July 9, 2024

Grounding AI Reduces Hallucinations and Increases Response Accuracy

Grounding AI is the process of connecting large language models to real-world data to prevent hallucinations and ensure more reliable and relevant outputs.

RAG

Chain-of-Thought Prompting 101

June 25, 2024

Chain-of-Thought Prompting 101

Chain-of-thought prompting is a technique that trains GenAI models to use step-by-step reasoning to handle complex tasks with greater accuracy and agility.

RAG

Data Readiness Can Make or Break Your GenAI Projects

June 16, 2024

Data Readiness Can Make or Break Your GenAI Projects

Data readiness is the ability to prove the fitness of data for generative AI use cases. Jean-Luc Chatelain told us how it affects enterprise GenAI adoption.

RAG

Generative AI Hallucinations: When GenAI is More Artificial than Intelligent

June 10, 2024

Generative AI Hallucinations: When GenAI is More Artificial than Intelligent

Generative AI hallucinations are incorrect or nonsensical GenAI outputs, resulting from flawed data or misinterpretations of data patterns during training.

RAG

GenAI Data: Is Your Enterprise Data Ready for Generative AI?

May 27, 2024

GenAI Data: Is Your Enterprise Data Ready for Generative AI?

What’s keeping you from realizing the full potential of generative AI? Your data believe it or not! Learn how to turn your enterprise data into GenAI data.

RAG

RAG Architecture: The Generative AI Enabler

May 20, 2024

RAG Architecture: The Generative AI Enabler

RAG architecture enables real-time retrieval and integration of publicly available and privately held company data that enhances LLM prompts and responses.

RAG

LLM Hallucination Risks and Prevention

May 12, 2024

LLM Hallucination Risks and Prevention

An LLM hallucination refers to an output generated by a large language model that’s inconsistent with real-world facts or user inputs. RAG helps avoid them.

RAG

AI Personalization: It’s All about You!

May 7, 2024

AI Personalization: It’s All about You!

AI Personalization combines Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to create personalized and satisfying user experiences.

RAG

What is an AI Hallucination?

May 5, 2024

What is an AI Hallucination?

An AI hallucination is an AI-generated output that’s factually incorrect, nonsensical, or inconsistent, due to bad training data or misidentified patterns.

RAG

What is Grounding and Hallucinations in AI?

May 1, 2024

What is Grounding and Hallucinations in AI?

Grounding is a method designed to reduce AI hallucinations (false or misleading info made up by GenAI apps) by anchoring LLM responses in enterprise data.

RAG

Gartner Generative AI: Shifting Gears to GenAI at the 2024 Data & Analytics Summit

April 16, 2024

Gartner Generative AI: Shifting Gears to GenAI at the 2024 Data & Analytics Summit

Each of the many Gartner D&A summits I’ve attended had its own theme. This year it was all about getting your data ready for GenAI. Here are my takeaways.

RAG

RAG Hallucination: What is It and How to Avoid It

April 10, 2024

RAG Hallucination: What is It and How to Avoid It

Although regular RAG grounds LLMs with unstructured data from internal sources, hallucinations still occur. Add structured data to the mix to reduce them.

RAG

Human in the Loop: Must There Always be One? Another AI Horror Story

March 21, 2024

Human in the Loop: Must There Always be One? Another AI Horror Story

With firms being held liable for their chatbot interactions, it's up to AI to ensure accurate answers. Having to rely a human in the loop is a non-starter.

RAG

RAG Conversational AI – Making Your GenAI Apps More Effective

March 19, 2024

RAG Conversational AI – Making Your GenAI Apps More Effective

Imagine users receiving fresh accurate info all the time. Retrieval-augmented generation optimizes conversational AI by injecting LLMs with enterprise data.

RAG

LLM Grounding Leads to More Accurate Contextual Responses

March 4, 2024

LLM Grounding Leads to More Accurate Contextual Responses

LLM grounding is the process of linking linguistic turns of phrase to the real world, allowing LLMs to respond more accurately than ever before.

RAG

Retrieval-Augmented Generation vs Fine-Tuning: What’s Right for You?

February 28, 2024

Retrieval-Augmented Generation vs Fine-Tuning: What’s Right for You?

When your LLM doesn’t meet your expectations, you can optimize it using retrieval-augmented generation or by fine-tuning it. Find out what's best, when.

RAG

Active Retrieval-Augmented Generation – For Even Better Responses

February 20, 2024

Active Retrieval-Augmented Generation – For Even Better Responses

Active retrieval-augmented generation improves passive RAG by fine-tuning the retriever based on feedback from the generator during multiple interactions.

RAG

RAG GenAI: Why Retrieval-Augmented Generation is Key to Generative AI

February 18, 2024

RAG GenAI: Why Retrieval-Augmented Generation is Key to Generative AI

RAG transforms generative AI by allowing LLMs to integrate private enterprise data with publicly available information, taking user interactions to the next...

RAG

LLM AI Learning via RAG Leads to Happier Users

February 14, 2024

LLM AI Learning via RAG Leads to Happier Users

By injecting private data into large language models, RAG enhances LLM AI learning for more personalized, precise, and pertinent answers to user queries.

RAG

What is Retrieval Augmented Generation

January 28, 2024

What is Retrieval Augmented Generation

Retrieval-augmented generation is a framework for improving the accuracy and reliability of large language models using relevant data from internal sources

RAG

Gartner LLM Report: RAG Tips for Grounding LLMs with Enterprise Data

January 28, 2024

Gartner LLM Report: RAG Tips for Grounding LLMs with Enterprise Data

Learn how to prepare for RAG with this FREE condensed version of the 2024 Gartner LLM report, “How to Supplement Large Language Models with Internal Data”.

RAG

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