Topic
Vector Databases
5 posts on vector databases — part of glossary on the n4n AI blog.
When you need a vector database (and when you don't)
A practical decision framework for choosing vector databases — when embeddings justify the complexity, and when simpler search works better.
What is a vector database, explained simply
A practical definition of vector databases covering embeddings, ANN search, filtering, and common misconceptions — written for engineers building LLM applications.
Pinecone vs Weaviate vs Milvus: which one to use
A practitioner's head-to-head comparison of Pinecone, Weaviate, and Milvus across architecture, performance, cost, and ergonomics — with a clear verdict for each use case.
FAISS explained: Meta's library for vector search
FAISS explained — how Meta's vector search library works, when to use it, and what engineers get wrong about indexing and quantization.
Choosing a vector database for your RAG pipeline
A practical guide to evaluating and selecting a vector database for RAG pipelines, covering indexing strategies, filtering, scaling, and common pitfalls.
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