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K2VIEW BLOG

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.

Iris Zarecki

Product Marketing Director

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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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K2VIEW BLOG

AI Data Fusion – New from K2view

Iris Zarecki

Explore more content