What the heck is an llm.txt

In the near future, humans won’t write code to integrate your SDK. They won’t read your API documentation. They won’t even visit your developer portal. Instead, AI agents will be your primary “developers.” They’ll discover your API, understand its capabilities, write integration code, and deploy solutions, all without human intervention.

What is llm.txt?

An llm.txt file is essentially a machine-readable developer reference that acts as documentation optimized for AI comprehension rather than human readability.

• It’s a lightweight, plain-text specification that documents your SDK or codebase so Large Language Models can quickly understand available functions, their inputs/outputs, and intended usage patterns.
• Unlike human-facing docs (like a README), it’s written to be structured, concise, and context-rich for AI models to parse efficiently.
• The file typically includes:
  • Function names + complete signatures
  • Parameters + types (with required/optional indicators)
  • Return values + structures
  • Plain English descriptions (no marketing fluff)
  • Minimal, working code examples

Think of it as a hybrid between TypeScript definitions + inline documentation, but flattened into a structured text file so LLMs can use it as a knowledge grounding artifact for integration tasks.

🔥Generate Your llm.txt with AI

A simple prompt to create machine-readable documentation from your existing codebase

You don’t need to write llm.txt from scratch. In my work, this is a prompt format that has been helpful as a base using Cursor or Claude to automatically generate one from an existing codebase:

You are tasked with generating an llm.txt file that documents and structures the [project name].

1. Read through the source code inside the `[project path]` and any related types, structs and data wrapper files. 

[optional] Refer to integration tests or suites for relevant code samples

2. Extract all available functions, methods, and exposed classes within stellar-sdk package
3. For each function, write a concise entry in the following format:

### Function Name
- **Signature:** <function signature with params + types>
- **Description:** What the function does in plain English
- **Inputs:** List of parameters (name, type, description)
- **Outputs:** Return type and meaning
- **Example Call:** Minimal code snippet showing usage

4. Organize the file into sections that match the SDK/API  functionality:

[list all functionalities and API methods here]

5. Keep all descriptions **concise and LLM-friendly** (short sentences, minimal jargon, direct explanations).

6. Save the result as `llm.txt` in the package root.

This file should serve as a **machine-readable developer reference** that an LLM can ingest to generate code completions and context-aware explanations.