When Large Language Models Meet Vector Databases: A Survey

Overview

This 2026 research survey presents a systematic review of recent advances in combinations of Large Language Models (LLMs) and vector databases. Published on arXiv (2402.01763) in January 2026, it addresses critical challenges in LLM applications.

Key Topics Covered

Integration Challenges

• Hallucination Mitigation: How vector databases provide factual grounding for LLM outputs
• Bias Reduction: Using diverse vector stores to balance LLM responses
• Real-Time Knowledge: Enabling LLMs to access current information through vector retrieval
• Context Management: Efficient handling of large context windows with vector search

Technical Approaches

• Retrieval-Augmented Generation (RAG) architectures
• Hybrid search strategies combining dense and sparse retrieval
• Vector database selection criteria for LLM applications
• Performance optimization techniques
• Cost-effectiveness analysis

Applications Analyzed

• Question-answering systems
• Chatbots and conversational AI
• Code generation and programming assistants
• Document analysis and summarization
• Enterprise knowledge management

Research Methodology

The survey analyzes:

• 100+ research papers from 2023-2026
• Production system architectures
• Benchmark comparisons
• Real-world deployment case studies

Key Findings

• Vector databases significantly reduce LLM hallucinations
• Hybrid retrieval approaches outperform pure vector or keyword search
• Proper chunking strategies critical for retrieval quality
• Cost-performance trade-offs vary significantly across use cases

Target Audience

• AI researchers and practitioners
• LLM application developers
• System architects designing RAG systems
• Graduate students in AI/ML

Citation

Published on arXiv, freely accessible for academic and commercial use.