Topic
Semantic Search vs Keyword Search
5 posts on semantic search vs keyword search — part of glossary on the n4n AI blog.
What is semantic search and how does it work
A practitioner's guide to semantic search — how vector embeddings replace keyword matching, the retrieval pipeline, and where it actually beats lexical search.
Semantic search vs keyword search: what's the difference
A practical comparison of semantic search and keyword search across capabilities, latency, cost, and ergonomics — with a decision framework for engineers choosing between them.
Semantic search explained with real examples
Semantic search explained with working code examples — how vector embeddings replace keyword matching, when to use each approach, and common pitfalls.
Building semantic search with OpenAI embeddings
A hands-on tutorial for building semantic search with OpenAI embeddings, covering vector storage, indexing strategies, and production considerations with runnable Python code.
BM25 explained: the algorithm behind keyword search
Understand the BM25 ranking function: term frequency saturation, inverse document frequency, and document length normalization with concrete examples.
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- 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