If you understand these 4 types, everything clicks 👇 🧠 Naive RAG Retrieve → send to LLM → answer Good starting point, but accuracy is limited. 🔀 Hybrid RAG Keyword + semantic search This is what most real-world systems use. 🔗 Graph RAG Understands relationships between data. Useful for complex queries. 🤖 Agentic RAG Plans → retrieves → reasons → iterates This is where things are heading. ⚡ Key insight: Better AI ≠ bigger model Better AI = better retrieval If you're building anything with LLMs,focus more on retrieval than prompts. That’s the real leverage. What are you currently using?

Not because of the models.But because of weak architecture, poor risk controls, and unclear business value. The key difference most teams miss: ➡️ Chatbots generate text. ➡️ Agents execute actions. Agents can call APIs, access databases, trigger workflows, and interact with critical systems. That architectural shift introduces serious security and reliability risks. Building a demo agent in a notebook? ⏱ A few hours. Deploying a production-grade AI agent? ⚙️ Real engineering. Some principles that separate production systems from fragile demos: • Define clear agent boundaries and threat models • Protect against prompt injection (still the #1 vulnerability) • Treat tools as strict typed contracts • Enforce RBAC and least privilege for tool execution • Keep context compact and intentional • Build observability, retries, and circuit breakers • Continuously evaluate for drift, safety, and reliability The reality is simple: AI agents are not prompt engineering problems. They are distributed systems problems. Teams that treat them like infrastructure will unlock real value. Everyone else will likely become part of the 40% failure statistic.

The challenge was to build an AI support agent using n8n that can automatically handle incoming customer emails. The agent needs to: • classify emails into categories (setup, pricing, security, HR, escalations, spam, etc.) • generate replies grounded in Nexus Integrations’ documentation • escalate emails to the correct team when required • return responses in structured JSON format The interesting part wasn’t just using an LLM — it was designing the workflow architecture around the AI. Here’s what I built: ⚙️ Email Classification Layer Incoming emails are categorized so the system understands the intent. 🤖 AI Support Agent Generates replies using a controlled knowledge base (pricing, integrations, security policies, escalation rules) to avoid hallucinations. 🚫 Spam & Misdirected Filtering Unrelated emails are filtered before they reach the AI agent. 📦 Structured Output Responses are formatted into JSON so they can be evaluated automatically. 📊 Automated Evaluation Pipeline A separate workflow sends test emails to the agent and scores responses using an LLM judge based on: - category correctness - documentation grounding - correct escalation handling Big learning from this challenge: 👉 Building AI systems is less about prompting and more about designing reliable workflows and guardrails around the model. Handling edge cases, grounding responses in documentation, and designing evaluation loops turned out to be the most important parts. Sharing the workflow architecture below 👇 Curious how others approached the challenge and structured their agents. #n8n #n8nchallenge #automation #aiagents

I built a workflow that automatically extracts invoice data from scanned PDFs and sends it to Google Sheets. Here’s how it works: • Sarvam Vision performs OCR on scanned invoices • Gemini extracts structured invoice fields • n8n pushes the cleaned data into Google Sheets So the pipeline becomes: 📄 Scanned Invoice → 🤖 AI extraction → 📊 Structured spreadsheet data This can be useful for automating: - Vendor invoice processing - Expense reimbursements - Bulk document intake - Finance / ops workflows And the best part — it's free to use on n8n. 🔗 Template link: https://n8n.io/workflows/13779 If anyone here is working on document automation or invoice processing, would love to hear how you're solving it. Happy to answer questions about the workflow as well 🙌

While exploring the latest updates in the AI agent ecosystem, I realized many people mix up these three concepts: • Prompting • MCP Servers • Claude Skills But they actually solve very different problems when building AI systems. A simple way to think about it: Prompting → tells the AI what you want MCP → gives the AI tools to access data Skills → define the workflow or SOP So instead of relying only on prompts, modern AI agents combine all three: Prompt → triggers the task MCP → retrieves the data Skills → execute the workflow That’s when an LLM moves beyond being just a chatbot and starts acting like a real AI teammate. Curious — are you experimenting with Claude Skills or MCP servers yet? Would love to hear what you're building. 🚀