Topic
Reranking & Hybrid Search
5 posts on reranking & hybrid search — part of glossary on the n4n AI blog.
What is a reranker and why RAG pipelines need one
A reranker is a cross-encoder model that re-scores retrieved documents for relevance, dramatically improving RAG answer quality over vector search alone.
Hybrid search explained: vector plus keyword search
A practitioner's guide to hybrid search: combining vector and keyword retrieval, fusion strategies, reranking, and production trade-offs.
How reranking fixes bad retrieval in RAG
A practical guide to adding reranking to your RAG pipeline — why vector search alone fails, how to pick and deploy a reranker, and how to verify it actually improves answers.
Cross-encoders vs bi-encoders for reranking
Cross-encoders vs bi-encoders for reranking: latency, quality, and operational trade-offs every search engineer needs to know.
Cohere Rerank explained: how it improves search results
A practitioner's guide to Cohere Rerank — what it is, how cross-encoder scoring works, when to use it over vector search alone, and the latency trade-offs you'll actually face in production.
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Fine-Tuning Fundamentals9
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88