The LLMGraph blog
Build AI workflows, no code required
Practical, hands-on guides to shipping RAG chatbots, customer support assistants, and AI agents.
How to Reduce LLM API Costs — A Practical Guide for Teams Shipping AI
Your LLM bill is mostly tokens you didn't need to send. Here's how teams cut API costs — model right-sizing, prompt caching, leaner input — without losing quality.
Read the guideRAG Chunking Strategies: How to Split Documents for Better Retrieval
Even a great model gives bad answers on bad chunks. Here's how to split your documents so retrieval surfaces the right context every time.
Read moreLLM Context Windows Explained: Tokens, Sizes, and How to Use Them Well
Every LLM request shares one budget for your prompt, your docs, the history, and the answer. Here's how context windows work — and how to use them well.
Read moreHow to Build a RAG Chatbot Without Code
A RAG chatbot answers from your documents instead of guessing. Here's how to build one — grounded, tested, and deployed — without writing any code.
Read moreHow to Add an AI Chatbot to Your Shopify Store Without an App
You don't need a Shopify app to put an AI assistant on your storefront. One script tag in theme.liquid does it. Here's the whole process, snippet included.
Read moreHow to Add an AI Customer Support Assistant — Without Writing Code
Adding an AI support assistant used to mean a quarter of engineering work. Here's how leaders ship one — grounded in their own docs — in an afternoon.
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