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This is your AI change management manual for business model enhancement. Here we map, index, and signpost every aspect of executive and organizational AI adoption to ensure you start smart and create value with artificial intelligence.
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LexisNexis is acknowledging it doesn't know everything by announcing a new AI product that retrieves data from various sources and then uses generative AI to make the output quicker, more useful, and more accurate. This approach leverages a concept called Retrieval-Augmented Generation (RAG). Essentially, it addresses the limitation that an AI model doesn't learn after it's released. For example, ChatGPT was trained on data up until October 2023. So, for it to “learn”, we must add data from other sources.

Today’s Daily Spark will explain how to incorporate this concept in your work and within a team workflow. We'll also discuss how to develop your organization's technology to gain a competitive advantage by adding this technology.

• LexisNexis Solving Information Overload

• Retrieval-Augmented Generation (RAG) Defined

• The Retriever Side of AI

• AI-enhanced Research Workflow

• Segment Focus: Technology Development

• Access and Incorporate Proprietary Content Case Study

Navigate AI News

LEXISNEXIS - RESEARCH
Solving Information Overload

Executive Brief: LexisNexis has launched Nexis+ AI, a new generative AI-powered tool designed to revolutionize business research and analysis. This platform aims to help corporate professionals quickly gather insights from vast amounts of data, including financial documents, earnings call transcripts, and news articles. Read more. Key features include:

• Rapid research analysis and document summarization

• Automated report generation

• Access to licensed content from over 20,000 titles

• Use of retrieval-augmented generation (RAG) for enhanced accuracy

• Content from outlets like Associated Press, Gannett, and McClatchy

TODAY’S TERM
Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation (RAG) is a method that combines information retrieval with generative models to enhance accuracy. Here’s how it works:

• Information Retrieval: The system first retrieves relevant documents or passages based on a query.

• Generative Model: A generative model, like GPT, uses the retrieved information to generate a coherent response.

RAG addresses the limitations of purely generative models, such as hallucinations, by grounding responses in real data. This improves the accuracy and relevance of the generated content, making it valuable for applications needing reliable information, like business research and legal analysis.

The Nexis+ AI tool leverages foundation models from Anthropic and OpenAI, combined with RAG technology to navigate content effectively. This approach allows LexisNexis to provide accurate, contextually relevant answers while respecting intellectual property rights and ensuring fair compensation for publishers.

AI INSTRUCTIONS

Prompt with Purpose
The Retriever Side of AI

To demonstrate the retrieval-augmented generation (RAG) concept used in Nexis+ AI, here are some example prompts that mimic its functionality. To retrieve information from the web, compare an AI-powered answer engine like Perplexity or You.com to a chat tool like ChatGPT to experience the difference.

Copy and paste the prompts below into Perplexity or You.com.

These prompts showcase how RAG enhances AI responses by retrieving and synthesizing information from multiple sources, providing users with comprehensive, fact-based insights for business decision-making.

Human Side of AI
AI-enhanced Research Workflow

Executive Brief: Research with RAG addresses key challenges faced by business teams, such as information overload and time constraints. It enables teams to assess new info quickly, evaluate opportunities, and make informed strategic decisions. Key benefits:

• Enhanced Information Retrieval: Efficiently scans and analyzes large documents, pinpointing relevant info and flagging issues.

• Improved Accuracy and Reliability: Reduces inaccuracies by grounding AI responses in retrieved factual information.

• Streamlined Document Drafting: Quickly generates initial drafts using templates and precedents.

• Contextual Understanding: Captures semantic and contextual nuances for accurate interpretation.

• Efficient Context Analysis: Summarizes key points and retrieves relevant information, saving time on research.

While RAG enhances efficiency and accuracy, it supplements but does not replace expertise, requiring careful use and human oversight.

Value Chain Enhancement
Segment Focus: Technology Development

Executive Brief: The market is booming with tools and products representing a range of options for implementing RAG, from open-source frameworks to enterprise-level solutions offered by major tech companies. Some examples:

• Pinecone: A vector database that can be used to store and retrieve embeddings for RAG implementations.

• Microsoft Azure AI Search: A cloud search service with AI-powered content mining capabilities that can be used for RAG implementations.

• Deep Lake: A vector database software application that can be used to store multi-dimensional data for RAG systems.

Scale Economics
Access and Incorporate Proprietary Content

Executive Brief: One notable case study of a company innovating with Retrieval-Augmented Generation (RAG) is Mintel, a leading market intelligence agency. Mintel recently launched a groundbreaking AI-powered solution called Mintel Leap, which leverages RAG technology to provide their clients with a transformative user experience for market research