AI Architecture
Re-Ranking with Cohere & Cross-Encoders: Supercharging RAG Precision by 40%
Implement two-stage retrieval: fetch 50 candidate chunks with fast vector search and re-rank the top 5 with Cohere Cross-Encoders.
Two-Stage Retrieval Architecture
1. Stage 1 (Bi-Encoder): Rapidly fetch 20-50 candidate documents with vector embeddings. 2. Stage 2 (Cross-Encoder): Deeply analyze query-document pairs simultaneously with Cohere Rerank to extract the top 3-5 most relevant chunks.
Related Technical Guides
Deepen your understanding with these closely related production architectures and tutorials:
2026 Frontier Stack: LangGraph Checkpointing, Mem0 Scoped Memory & Prefix Caching FinOps
Architect enterprise AI agents with decoupled lifecycles: prompt KV-cache optimization (90% savings), LangGraph state persistence, and Mem0 long-term memory.
Model Context Protocol (MCP) Guide: Connecting LLMs to Local Databases and Tools
Learn the open-source Model Context Protocol (MCP) standard created by Anthropic and how it turns LLMs into extensible agents connected to your infrastructure.
Prompt Caching Architecture: Slashing LLM API Costs and Latency by 90% via KV-Cache Reuse
Master Anthropic and Gemini Prompt Caching to slash API bills and reduce latency on long documents, system instructions, and multi-turn chats.