Reranker Playground
Retrieve candidates, then re-rank them and watch the most relevant results jump to the top.
How to Use This Tool
Enter Your Query
Type any question in the text area or click one of the example queries below
Watch the Process
See how vector search finds initial results and the reranker analyzes them for better relevance
Compare Results
Observe how reranking improves result quality with rank changes and quality metrics
Learn from Metrics
Review improvement metrics to understand how rerankers enhance RAG system performance
Reranking in Action
1. Enter Your Query
Ask a question to see how rerankers improve retrieval results
Try These Examples
Click any example to see reranking in action
Specialized Judge
A reranker is a smaller, specialized model that acts as a final judge in the retrieval process. It analyzes the relationship between your query and each retrieved document chunk.
Precise Scoring
While vector search finds semantically similar content, rerankers evaluate true relevance by understanding context, intent, and query-specific importance.
Result Optimization
It re-orders the top vector search results to push the absolute best, most relevant chunks to the top before they're sent to the LLM for generation.
Context Enhancement
This dramatically improves the quality of context the LLM receives, leading to more accurate, relevant, and helpful responses.
1. Initial Retrieval
Vector search finds the top-k most semantically similar documents (e.g., top 20-100 results)
2. Cross-Encoder Analysis
The reranker model processes query-document pairs together, computing precise relevance scores
3. Score Calculation
Each document receives a new relevance score based on deeper semantic understanding and context
4. Result Reordering
Documents are reordered by their new scores, with the most relevant chunks moved to the top
5. Context Optimization
The top-ranked results (e.g., top 3-5) become high-quality context for the LLM to generate responses
Improved Relevance
Rerankers understand nuanced query intent better than vector similarity alone, leading to more relevant results.
Higher Precision
Cross-encoder models provide more accurate relevance scoring by processing query and document together.
Better LLM Context
LLMs receive higher-quality, more relevant context, resulting in more accurate and helpful responses.
Cost Efficiency
Better context means fewer tokens needed and less need for multiple retrieval attempts.
Domain Adaptation
Rerankers can be fine-tuned for specific domains or use cases for even better performance.
Noise Filtering
Helps filter out irrelevant or tangentially related content that vector search might include.
Cross-Encoder Models
Process query and document together through transformer layers for precise relevance scoring.
- ms-marco-MiniLM-L-6-v2
- bge-reranker-large
- cohere-rerank-v3
Lightweight Rerankers
Faster models optimized for real-time applications with good performance-speed tradeoffs.
- Real-time search applications
- Mobile and edge deployment
- High-throughput systems
Domain-Specific Rerankers
Models fine-tuned for specific domains like healthcare, legal, finance, or technical documentation.
- Domain expertise understanding
- Specialized terminology handling
- Context-aware relevance scoring
Optimal Pipeline Configuration
Retrieve 20-100 documents with vector search, then rerank to get the top 3-10 most relevant ones for the LLM.
Performance vs. Quality Balance
Choose reranker models based on your latency requirements. Larger models offer better quality but higher latency.
Evaluation and Monitoring
Continuously evaluate reranker performance using metrics like MRR, NDCG, and end-user satisfaction.
Query Understanding
Some queries benefit more from reranking than others. Consider query complexity and ambiguity levels.