> ## Documentation Index > Fetch the complete documentation index at: https://developer.uphold.com/llms.txt > Use this file to discover all available pages before exploring further. # Building with AI > Use the Uphold Enterprise docs with LLMs and AI coding tools. Feed llms.txt and llms-full.txt to ChatGPT, Claude, Cursor, or your own agents to scaffold integrations faster. AI assistants are how developers read docs now. The Uphold Enterprise documentation is built to be consumed by large language models as easily as by people — so you can drop our entire API surface into ChatGPT, Claude, Cursor, or your own agent and start building in minutes. ## What's inside the files We follow the [llms.txt standard](https://llmstxt.org) and publish two complementary files. Pick the one that fits your context window and task. A **structured index** of the entire documentation — every guide, API reference, and concept as a titled, described link. Best for giving an assistant a map of what exists so it can fetch the right page on demand. The **complete documentation** concatenated into a single file — every page's full content in one place. Best for dropping the whole knowledge base into a large context window for deep, end-to-end work. Between them, your assistant gets full coverage of: Account setup and your first API call. KYC, KYB, and capabilities. FPS, SEPA, ACH, FedNow/RTP, Wire. Debit & credit deposits and withdrawals. 50+ networks, deposits and withdrawals. Every endpoint, payload, and webhook. Payment, Travel Rule, and KYC widgets. Dynamic forms, errors, pagination, rate limits. ## Why build with AI? Generate working integration code for onboarding, deposits, withdrawals, and webhooks without leaving your editor. Ground your assistant in the real API contract so it stops hallucinating endpoints and parameters. The files are generated from this site, so your AI context updates the moment the docs do. ## How to use llms-full.txt The fastest way to get answers grounded in our docs. Open [developer.uphold.com/llms-full.txt](https://developer.uphold.com/llms-full.txt) and copy its contents, or download it. Start a new conversation and paste the file (or attach it). Then ask your question — for example: > Using the attached Uphold docs, write a Node.js function that onboards an individual US user and sets up an ACH deposit method. Follow up naturally. Because the model has the full API contract, it can refine payloads, add error handling, and wire up webhooks accurately. Hitting context limits? Use [llms.txt](https://developer.uphold.com/llms.txt) instead — it's a compact index the model can use to request only the specific pages it needs. Bring the docs directly into your coding workflow. In Cursor, run **`@Docs` → Add new doc** and paste `https://developer.uphold.com/llms-full.txt`. Other AI IDEs offer a similar "add documentation / custom context" option. Mention the doc source in your prompt (e.g. `@Uphold`) and ask the assistant to scaffold or debug an integration inline. Fetch the file at runtime to ground your own LLM application or agent. ```bash cURL theme={null} curl https://developer.uphold.com/llms-full.txt ``` ```python Python theme={null} import requests docs = requests.get("https://developer.uphold.com/llms-full.txt").text # Pass `docs` as system/context to your model of choice ``` ```javascript Node.js theme={null} const res = await fetch("https://developer.uphold.com/llms-full.txt"); const docs = await res.text(); // Pass `docs` as system/context to your model of choice ``` `llms-full.txt` is large. For token-sensitive or production agents, prefer fetching [llms.txt](https://developer.uphold.com/llms.txt) as an index and pulling individual pages on demand — append `.md` to any docs URL to get its raw Markdown. ## Next steps End-to-end walkthroughs for onboarding and money movement. Embeddable Payment, Travel Rule, and KYC widgets. Full reference for every endpoint, payload, and webhook.