The Critical Role of Enterprise Search for Large Language Models
Executive Summary
Large language models have shown impressive natural language capabilities. However, these models still face gaps without access to relevant, high-quality enterprise information. This white paper makes the case for why enterprise search is instrumental for improving model accuracy and reliability. It also explores how AI is upgrading enterprise search - creating a symbiotic collaboration.
Key Highlights:
Enterprise search delivers training data for model integrity
High-quality results prevent model hallucination
Optimized search builds model honesty through evidence
A virtuous loop between enterprise search and models unlocks new capabilities
Table of Contents
Introduction
Why Enterprise Search Matters for Models
Limitations of Current Enterprise Search
AI to the Rescue
Use Cases and Benefits
Challenges and Considerations
The Self-Improving AI-Search Loop
Introduction
Recent advances like ChatGPT demonstrate remarkable natural language prowess. However, despite the hype, these early models still face gaps without access to relevant, trustworthy enterprise information.
This white paper makes the case for why enterprise search is critical for improving model accuracy and trustworthiness. It also explores how AI is transforming enterprise search - creating a symbiotic collaboration.
Why Enterprise Search Matters
As conversational AI evolves, enterprise search brings three key benefits:
1️⃣ Relevant Training Data:
High-quality results supply real-world grounding to prevent hallucination.
2️⃣ Accuracy Benchmarking:
Understanding relevance provides supervision for model learning.
3️⃣ Concept Evolution:
Analyzing search patterns reveals emerging interests and semantics.
Without access to relevant enterprise information, models spin fiction unsupported by evidence. But integrated with enterprise search, they learn to produce helpful, harmless and honest responses grounded in reality.
Limitations of Current Enterprise Search
While enterprise search promises to unleash insights, some inherent gaps remain using legacy approaches:
Search Relevance: Low precision and recall degrade usability
Information Silos: Fragmented data prevents single views
Query Understanding: Unraveling intent is non-trivial
Legacy Mindsets: Solutions fail to meet modern expectations
These pitfalls lead to business risks from securing information to delivering experiences. Recent innovations aim to address these gaps with automation.
AI to the Rescue
Advances in search technology allow AI to help transform enterprise search itself:
Vertical Expertise: Domain-specific augmentations boost recall
Query Understanding: Discovering semantics and intent
Knowledge Connections: Identifying upstream signals and relationships
Result Clustering: Grouping contextually related information
Automating manual efforts allows experts to orchestrate reliable enterprise information retrieval - powering helpful and honest language models while unlocking productivity.
Use Cases and Benefits
Common scenarios seeing strong impact from AI-powered enterprise search:
2X higher search efficiency from improved ranking
30% faster query understanding through intent parsing
50% increased findability by connecting fragmented information
The collective impact results in models grounded in reality through relevant enterprise evidence - critical for business use.
Challenges and Considerations
Operationalizing AI for enterprise search requires addressing factors like:
Hybrid Governance: Balance automation with human oversight
Responsible AI: Ensure model transparency and accountability
Change Management: Encourage adoption across stakeholders
Compliance: Meet regulatory requirements around information handling
The Self-Improving AI-Search Loop
Looking ahead, an auto-curative loop between enterprise search and language models creates exponential capability gains - preventing divergence while elevating reasoning through this symbiosis.
With optimized access to relevant enterprise knowledge, models hold promise as reliable assistants grounded in organizational reality - transforming expertise access and sharing.