Topic
Prompt Engineering Fundamentals
6 posts on prompt engineering fundamentals — part of glossary on the n4n AI blog.
What is prompt engineering and why it still matters
A precise definition of prompt engineering, how it shapes LLM behavior, why it remains essential despite model improvements, and practical patterns engineers use daily.
Prompt engineering basics: a beginner's guide
A practical guide to prompt engineering basics covering core techniques, structured prompting patterns, and common pitfalls for engineers building LLM applications.
How to structure a prompt for consistent outputs
A step-by-step guide to structuring prompts that produce reliable, repeatable LLM outputs — with runnable validation code and a checklist you can drop into CI.
How temperature and top-p affect your prompts
A practical guide to temperature and top-p prompting with code examples, common pitfalls, and tradeoffs for engineers building LLM applications.
Common prompt engineering mistakes and how to fix them
Seven prompt engineering mistakes that break production LLM systems, with concrete fixes and code patterns you can use today.
10 prompt engineering techniques that actually work
Ten battle-tested prompt engineering techniques with code examples for engineers building production LLM systems.
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