AI Search General Workflow: Understanding the Process
AI Search started from the very simple idea of summarizing search results (from Google or similar search engines) using LLM (Large Language Models):
AI Search is based on the simple idea of postprocessing results from search engine (like Google) with Large Language Model (LLM)
But then it evolved into a more complex system that follows the following workflow:
The AI Search Process Workflow
AI search follows a systematic workflow designed to deliver accurate, comprehensive results:
Query Input
- Users enter their search query
- Can be questions, phrases, or keywords
- System accepts natural language input
Preprocessing
- LLM rewrites/decomposes the question
- Optimizes query for search performance
- Breaks complex queries into sub-components
Search Execution
Search results are gathered from:
BingGoogle- Platform's own index (e.g.,
Perplexity AI)
Content Summarization
- LLM analyzes source content
- Synthesizes information from multiple sources
- Creates coherent, comprehensive answers
Follow-Up Generation
- AI generates relevant follow-up questions
- Helps users explore topics in greater depth
- Suggests related areas of interest
Feedback Loop
- Users can vote on answer quality
- Human annotators review responses
- Continuous system improvement
Technical Implementation
The workflow demonstrates how AI search combines traditional search capabilities with advanced language processing:
- Query preprocessing ensures optimal search parameters
- Multiple data sources provide comprehensive coverage
- AI synthesis creates user-friendly responses
- Feedback mechanisms enable continuous improvement
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Part of "The Future of SEO in the Age of AI-Driven Search" series.
