Articles

RAG the Easy Way in n8n n8n ships first‑class AI nodes for text splitting, embeddings, vector stores, retrievers, chains, and even reranking. That gives you a complete, click‑together stack instead of glue code. Examples include Default Data Loader, Character / Recursive / Token Text Splitters, OpenAI / Cohere Embeddings, Vector Store nodes for PGVector / Supabase / Pinecone / Weaviate / Qdrant, Vector Store Retriever, Question & Answer Chain, and Cohere Reranker.   Read More AI Articles     The Blueprint (one screen in n8n) Ingest + Chunk: Default Data Loader -> Recursive Character Text Splitter (or Token Splitter for

We all know this problem. Support@ inboxes turn into noise. Tickets pile up. Engineers get pinged for the wrong things. SLAs slip because the queue is a maze of duplicates, low‑value asks, and “just checking in” emails. Meanwhile the truly urgent issues wait their turn. There’s a better path: move triage to where your team already lives, Slack, and let AI do the sorting, scoring, and drafting. Humans stay in the loop for judgment calls. Engineering only sees the signal. The idea in one line Pipe your inbox (and help desk) into Slack. Use AI to label, prioritize, route, and

Modern inbound is noisy. Forms, chat, events, and trials all feed the same pipe. The fix isn’t another widget. It’s a clean, opinionated flow that turns raw submissions into enriched, de‑duplicated, explainable records your reps can trust. The outcome you’re aiming for Reps see one record per person or account, not six. Every record has a fit label, an intent label, a normalized score from 0–100, and a clear “why.” Marketing gets analytics on what sources and messages drive qualified demand. RevOps gets fewer merges and cleaner attribution. Legal gets a data map and consent trail. The Flow Intake: Capture

Your brief is the source of truth. Include Audience, problem to solve, POV in one sentence, promise of value, key proof points, non‑negotiables, CTA, due date, owner. Brief template Title/Working Angle: Audience: Problem we’re solving: Our POV (one sentence): Promise (what they gain in 60–90 sec): Proof (data, examples, quotes to use): Voice notes (tone, banned words, phrases to keep): CTA: Publish date/time: Owner + reviewer: Research: fill the “source bag” Collect 3–5 credible receipts. Pull one customer quote or real‑world example. Note a counterpoint you can address. Save links in the brief so they travel with the work. Outline

Quick automations die when someone inserts a column, renames a header, or sorts the sheet. Position-based writes hit the wrong cells. Formulas spill and you lose a weekend. However, the fix is simple: contracts over columns and ID-based upserts. Minimal column set for a resilient loop Create these headers. Names must stay the consistent. record_uuid source_fields (comma-separated list of the inputs you send to AI, or keep normal inputs in their own columns) ai_raw_json normalized_title category tags confidence schema_version model_version ai_status ai_error input_hash last_processed_at Add or reorder any other columns as you like. The loop will not care. The JSON

LLMs feel random when they’re treated like text boxes. They feel reliable when they’re treated like nodes in a workflow with contracts, control loops, and safety rails. In this article, we’ll explore how to move from prompt luck to system design using four durable patterns: function calling, JSON schemas, retries that repair, and guardrails. What’s an “LLM node” Think of each LLM step as a node in a graph. It takes a structured input, runs a bounded task, and emits a structured output. You can test it. You can measure it. You can swap it without breaking downstream systems. In

If you’re hand‑editing prompts, hard‑coding URLs, or nudging AI settings every time you deploy, you’re doing extra work. n8n Expressions let you template once, map fields cleanly, and ship workflows that behave the same in dev, staging, and prod. What “Expressions” really give you Expressions make any node parameter dynamic. You can pull values from earlier nodes, the running workflow, or your environment and then transform them with plain JavaScript. Think {{ $json.title }}, {{ $node[‘Search’].json.results[0] }}, or {{ $env.BASE_URL }}. Core sources you’ll use most $json for the current item’s data. $node[‘Node Name’]…or the shorthand $(‘Node Name’).item.json to reach

Pick n8n Cloud for the fastest start or Docker if you want full control. Add OpenAI credentials in n8n. Build a 4‑node flow: Webhook → OpenAI Chat Model (JSON) → Structured Output Parser → Respond to Webhook. Return clean, valid application/json to your caller. 1) Choose where to run n8n Option A: n8n Cloud Spin up an editor in minutes, no servers to maintain. If you’re new to n8n or want a low‑ops path, start here. Option B: Self‑host with Docker If you need custom networking, private nodes, or compliance controls, use Docker/Compose. n8n’s guide includes a production‑ready Compose file

You don’t need a thousand microservices to deliver AI value. Use n8n to orchestrate, add small bits of code where it matters, and wrap the LLM in guardrails. This opener gives you a decision rubric, a simple mental model, and the full series map so you can move fast without breaking your ops. AI can draft, summarize, and classify. n8n can trigger, schedule, retry, log, and connect to everything. Blend them well and you get output that’s useful on day one and maintainable on day ninety. Blend them poorly and you end up babysitting flows at 2 a.m. This series