Based on Trey Grainger’s keynote at Haystack EU 2024

The field of information retrieval is experiencing a revolutionary transformation. As AI becomes increasingly central to search technology, we’re witnessing the emergence of new terminology and techniques that are reshaping how we think about search engines, vector databases, and intelligent information systems.

The Evolution of Search Intelligence

Reflected Intelligence: The Foundation of Smart Search

At the heart of modern AI-powered search lies the concept of reflected intelligence – essentially sophisticated feedback loops that allow search systems to learn from user behavior. This isn’t just theoretical; it’s a practical approach that transforms how search engines understand and respond to user intent.

The process is elegantly simple yet powerful:

1. Users perform searches and interact with results
2. Systems collect behavioral signals (clicks, purchases, time spent)
3. Machine learning models process these signals
4. Improved search results are delivered based on learned patterns

Four Pillars of Behavioral Learning

1. Signals Boosting: Popularized Relevance

This technique focuses on learning exactly what users want for specific queries. By analyzing which results users actually click on and engage with, search engines can boost the most relevant documents to the top. It’s essentially letting your users tell you the right answers through their actions.

2. Learning to Rank: Generalized Relevance

Moving beyond specific queries, learning to rank creates generalized models that can improve search results across all queries. By using behavioral data to automatically generate relevance judgments through click models, these systems continuously improve their understanding of what makes content relevant.

3. Collaborative Filtering: Personalized Relevance

This approach learns about individual users’ preferences and behaviors to deliver personalized search results and recommendations. Using matrix factorization techniques, systems can identify patterns in user behavior to predict what specific users might find relevant.

4. Knowledge Graph Learning: Domain-Specific Intelligence

By mining query logs and user interactions, search engines can automatically discover relationships between terms, identify common misspellings, and build semantic understanding of domain-specific language.

The Vector Database Revolution

The distinction between search engines and vector databases is rapidly blurring. What we’re seeing is that vector search isn’t really a product category – it’s a feature that’s being rapidly adopted across all types of search systems. Every major search platform is adding vector capabilities, making the traditional categorization less meaningful.

Understanding Modern Search Techniques

Bi-encoders vs. Cross-encoders

Bi-encoders work by independently encoding queries and documents into the same vector space, allowing for efficient similarity matching. They’re fast and scalable but encode information separately.

Cross-encoders take a different approach by encoding queries and documents together, allowing for much richer contextual understanding. While slower, they provide significantly better relevance because the query context influences how documents are interpreted and vice versa.

The practical approach often involves using bi-encoders for initial retrieval and cross-encoders for reranking the top results.

The Spectrum of Semantic Search

Modern search exists on a spectrum from pure lexical matching to dense vector search, with numerous hybrid approaches in between:

• Term Expansion: Automatically learning expanded representations for lexical search
• Sparse Retrieval + Dense Reranking: Combining traditional search with vector-based rescoring
• Hybrid Search: Using techniques like reciprocal rank fusion to combine multiple search approaches
• Semantic Knowledge Graphs: Leveraging inverted indexes as graphs for probabilistic traversal

Optimization and Efficiency

Vector Quantization Techniques

As vector search scales, efficiency becomes crucial. Several quantization techniques help reduce memory usage while maintaining search quality:

• Scalar Quantization: Reducing float32 vectors to 8-bit integers, achieving 75% memory reduction with 93% recall retention
• Binary Quantization: Using single bits per dimension, achieving 97% memory reduction with 60% recall (recoverable through reranking)
• Product Quantization: Offering flexible compression levels by clustering sub-vectors

Matryoshka Embeddings

These hierarchical embeddings allow you to truncate vectors at different dimensions while maintaining reasonable recall, providing flexibility in balancing performance and accuracy.

The Multimodal Future

Modern search is expanding beyond text to encompass multiple modalities:

• Text + Images: Searching across different content types seamlessly
• Behavioral Embeddings: Incorporating user behavior as another modality alongside content
• Late Interaction Models: Techniques like ColBERT that create contextualized embeddings for each word while maintaining document-level understanding

Looking Ahead: Search as the Data Layer for AI

Perhaps most significantly, search is evolving from a standalone system to become the fundamental data layer that powers AI applications. As foundation models become more prevalent and gain physical embodiments through robotics, search systems will serve as the critical infrastructure that enables these models to access relevant, up-to-date information through RAG (Retrieval-Augmented Generation) and similar techniques.

Key Takeaways for Practitioners

1. Layer Your Approaches: Don’t think of different search techniques as mutually exclusive. Signals boosting, learning to rank, personalization, and knowledge graphs work best when layered together.
2. Embrace the Spectrum: Modern search isn’t binary between lexical and semantic. There’s a rich spectrum of hybrid approaches that can be tailored to your specific use case.
3. Think Beyond Categories: The lines between search engines, vector databases, and recommendation systems are blurring. Focus on the capabilities you need rather than product categories.
4. Prepare for Multimodal: The future of search involves multiple modalities working together, including user behavior as a first-class signal alongside content.
5. Consider Search as Infrastructure: As AI becomes more prevalent, position your search systems as the data layer that will power the next generation of AI applications.

The evolution of search technology represents one of the most exciting frontiers in AI today. By understanding these emerging patterns and techniques, organizations can build more intelligent, responsive, and effective search systems that truly understand and serve their users’ needs.

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This blog post is based on Trey Grainger’s keynote presentation at Haystack EU 2024. Trey is the lead author of “AI-Powered Search” (Manning 2024) and founder of Searchkernel, a consultancy specializing in next-generation AI-powered search systems.