This summary shows you how to set up efficient RAG (Retrieval Augmented Generation) systems with n8n templates and optimize your workflow – without in-depth technical knowledge or programming effort.
AI Articles & Tutorials
Our tutorials show step by step how to use AI technologies — from prompt engineering to AI programming and the use of smart tools for automation
Overview: Vector databases for AI projects
TL;DR
Vector databases store data as high-dimensional vectors (embeddings), enabling semantic search — finding results by meaning, not just keywords.
- Core use case: Power RAG systems, recommendation engines, and semantic search
- Top options: Pinecone (managed), Weaviate (open source), ChromaDB (lightweight), Qdrant (Rust-based)
- How it works: Text → Embedding model → Vector → Stored in DB → Similarity search via cosine/dot product
Bottom Line: If you’re building any AI application that needs to search or retrieve information, you need a vector database.
If you want to create AI applications such as a semantic search, you need a suitable vector database. These make embeddings available at lightning speed and scale better than classic SQL or NoSQL stores. We present popular options from SaaS to open source – including strengths, costs and typical use cases.
The 10 best n8n workflow templates for automation
n8n workflow templates significantly accelerate your automation work by providing preconfigured solutions for common business processes. With the right templates, you can immediately become more productive without having to create every automation from scratch. To understand the engine behind these workflows, read how n8n works under the hood.
n8n functions at a glance: What workflow automation offers
n8n is a powerful workflow automation platform that is characterized by its flexible open source architecture and intuitive interface. The following key points give you an overview of the most important functions and possible applications: The visual workflow creation allows you to connect different services by simple drag-and-drop without any programming knowledge, which greatly simplifies … Read more
Prompting in German: The ultimate guide to AI instructions
This guide will help you to get better results from AI systems with prompts in German. The following key points will show you how you can make the most of the special features of the German language for your AI instructions. German prompts now work excellently with modern AI models and often deliver more precise … Read more
How n8n works: Detailed insight into workflow automation
TL;DR How n8n works — in 60 seconds Node-based engine: Every automation step is a visual node — trigger, action, or logic. You connect them on a canvas like a flowchart, no code required. JSON data flow: Nodes pass structured JSON to each other. You can inspect, filter, and transform this data in real time … Read more
n8n troubleshooting: 7 common errors to fix quickly
This guide to n8n troubleshooting shows you systematic solution strategies for the most common workflow automation problems. With these practical tips, you can quickly fix errors and significantly increase the reliability of your n8n instance. Use debug nodes as the most important tool for error analysis and place them after suspicious nodes to examine data … Read more
Google AI Mode: Adapting digital marketing for AI search engines & Google AI Mode
With the AI Mode announced at Google I/O 2025, Google will become the AI search engine with the largest reach worldwide. Here you can find out what this means for marketing and how you should react.
Fine-tuning OpenAI’s GPT model – benefits and coding guide
Fine-tuning allows a language model to adapt to your own specific data, significantly improving response quality. Here, we demonstrate how to enhance OpenAI’s GPT-3.5 Turbo using fine-tuning through Python and the OpenAI API or Microsoft Azure. Highlight: we’ve provided a link to the complete Jupyter notebook.
Raw data reporting: Preparing GA4 data with BigQuery and Dataform
GA4 reporting based on raw data makes marketing reporting much more accurate and flexible to model. In this tutorial, we show you how to prepare GA4 data with Dataform in Google BigQuery.