Building High-Performance Search to Power AI Applications
Executive Summary
Many artificial intelligence use cases like conversational interfaces depend on efficient access to knowledge - whether documents, media or data. This makes building specialized search engines foundational before integrating capabilities like reasoning augmented generation (RAG). This white paper outlines best practices for developing and evaluating performant search ingesting enterprise content powering reliable AI systems.
Introduction
From natural language queries to recommendations, much of applied artificial intelligence links unstructured user input with relevant structured enterprise knowledge - whether product specifications, legal codes or textual corpus. However, finding precise information first requires specialized search technology at scale - the blocking and tackling preceding intelligent interfaces.
This white paper outlines pragmatic tactics for developing high-performance search ingesting enterprise content while retaining governance needed to understand model behavior based on retrievals.
Beyond Magic Memory
A common pitfall frames vector databases as expanding limited language model memory through similarity search. However, viewing vector query as a special case of search shifts focus from magic memory augmentation to purpose-built engines intelligently mapping signals to outcomes.
For instance, querying documentation requires search performing adequately for human self-service before integration. Framing vector databases as search optimization prevents confusion from introducing new failure modes like hallucination from irrelevant or adversarial retrievals.
Combined Strengths
Recent innovations ease previously specialized search development:
🔎 Keyword + Vector: Blending strengths prevents false negatives improving breadth
💬 Query Understanding: Models compose natural language search boosting recall
📈 Result Ranking: Lightweight relevance training outperforms rules-based logic
However, fundamentals like scoping remain - preventing unconstrained open-domain search degrading downstream utility through retrieval volatility across contexts, users and sessions.
Considerations
Success depends on addressing factors like:
Defining Scope: Bound ingestion to target reliability over coverage
Monitoring Usage: Track precision and latency guiding optimization
Sandboxing Access: Phase integrations preventing production issues
For builders, solving search unlocks reliable question answering, recommendation and automation - making foundational fluency imperative before engaging augmented intelligence.