Topic
Grounding & Fact-Checking in AI
8 posts on grounding & fact-checking in ai — part of glossary on the n4n AI blog.
What is grounding in AI, and why does it matter
A practitioner's guide to grounding in AI — what it is, how retrieval and tool use anchor model outputs to verifiable sources, and why it matters for production LLM systems.
Retrieval-augmented generation explained simply
A practitioner's guide to retrieval-augmented generation (RAG): how it works, why it matters for grounding LLMs, a working code example, and the misconceptions that trip up engineering teams.
How RAG grounds LLM responses in real data
RAG retrieves relevant documents and injects them into the prompt so the model answers from your data, not its training cutoff.
How Google's Gemini grounds answers with search
How Gemini grounding with Google Search works under the hood — retrieval, citation, and the trade-offs engineers should understand.
Grounding vs fine-tuning: what's the difference
A practical comparison of grounding and fine-tuning for LLM applications, covering when to use each approach across cost, latency, accuracy, and maintenance dimensions.
Grounded vs ungrounded AI responses: how to tell
Understand the difference between grounded and ungrounded AI responses, when each applies, and how to detect hallucination in production systems.
Fact-checking AI outputs: tools and techniques
A practical guide to fact-checking AI outputs with tools, techniques, and code patterns engineers can deploy today.
Citation grounding: making AI show its sources
Citation grounding forces LLMs to link every claim to a verifiable source document — here's how it works, why it matters, and what breaks when you skip it.
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
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88
- Chain-of-Thought Prompting7