Instead of searching Stack Overflow or GitHub, developer are asking ChatGPT "What's the best email API for Node.js?" or Claude "How do I generate videos from images in Python?" The answers they get depend on what these models learned during training—and how well your documentation teaches them.

LLM SEO is the process of optimizing content for AI chatbots and language models, just as traditional SEO optimizes for search engines. If your SDK documentation isn't structured for machine understanding, you're missing a growing channel for developer acquisition.

The Rise of LLM SEO: From Keywords to Context

Back in 2024, the concept was called "GEO" (Generative Engine Optimization), but now in 2025, with ChatGPT, Claude, Perplexity, DeepSeek, and other LLM-based search experiences making significant progress, we're looking at a fundamental shift in how content gets discovered.

Traditional SEO was simple: stuff keywords, build backlinks, hope for the best. But AI-first interfaces like ChatGPT and Google's AI Overviews now answer questions before users ever click a link. The old rules don't apply.

LLMs don't rank pages like search engines do. Instead, they analyze patterns in text to predict what comes next, using training data from a massive chunk of the internet to form these patterns. This means your content needs to be structurally sound, semantically rich, and contextually complete.

The shift is already happening. Research shows that LLM-based search is less about the number of inbound links and more about targeted content. Companies that understand this are winning. Those that don't are becoming invisible.

The Resend Case Study: How SDKs Drive Adoption

Resend demonstrates this shift clearly. Zeno Rocha's team went from 25,000 new users per month in January 2024 to 70,000 per month by late 2024. As Rocha noted: "It's pretty clear that we have a new definition of a 'developer' now."

Weekly signups at Resend:

This growth coincided with the mainstream adoption of AI coding assistants. Resend built comprehensive SDK coverage across multiple languages with clear, complete documentation. When developers ask AI assistants about email APIs, Resend gets recommended not because they gamed the system, but because their documentation provides the context and examples that LLMs need to understand when and how to suggest their service.

How Sideko Generates LLM-Friendly SDK Documentation

This is where proper SDK documentation becomes critical. Let's look at how we approach this with a real example from Magic Hour, a Sideko customer.

Here's their Rust SDK documentation for the image-to-video endpoint from: https://github.com/magichourhq/magic-hour-rust/blob/main/src/resources/v1/image_to_video/README.md

### Image-to-Video <a name="create"></a>

Create a Image To Video video. The estimated frame cost is calculated using 30 FPS. This amount is deducted from your account balance when a video is queued. Once the video is complete, the cost will be updated based on the actual number of frames rendered.
  
Get more information about this mode at our [product page](<https://magichour.ai/products/image-to-video>).
  
**API Endpoint**

#### Parameters

| Parameter | Required | Description | Example |
|-----------|:--------:|-------------|--------|
| `assets` | ✓ | Provide the assets for image-to-video. | `V1ImageToVideoCreateBodyAssets {image_file_path: "api-assets/id/1234.png".to_string()}` |
| `end_seconds` | ✓ | The total duration of the output video in seconds. | `5.0` |
| `height` | ✗ | This field does not affect the output video's resolution. The video's orientation will match that of the input image.  It is retained solely for backward compatibility and will be deprecated in the future. | `960` |
| `name` | ✗ | The name of video | `"Image To Video video".to_string()` |
| `resolution` | ✗ | Controls the output video resolution. Defaults to `720p` if not specified.  **Options:** - `480p` - Supports only 5 or 10 second videos. Output: 24fps. Cost: 120 credits per 5 seconds. - `720p` - Supports videos between 5-60 seconds. Output: 30fps. Cost: 300 credits per 5 seconds. - `1080p` - Supports videos between 5-60 seconds. Output: 30fps. Cost: 600 credits per 5 seconds. **Requires**

let client = magic_hour::Client::default()
    .with_bearer_auth(&std::env::var("API_TOKEN").unwrap());
let res = client
    .v1()
    .image_to_video()
    .create(magic_hour::resources::v1::image_to_video::CreateRequest {
        assets: magic_hour::models::V1ImageToVideoCreateBodyAssets {
            image_file_path: "api-assets/id/1234.png".to_string(),
        },
        end_seconds: 5.0,
        height: Some(960),
        name: Some("Image To Video video".to_string()),
        width: Some(512),
        ..Default::default()
    })
    .await;

#### Response

##### Type
[V1ImageToVideoCreateResponse](/src/models/v1_image_to_video_create_response.rs)

##### Example
`V1ImageToVideoCreateResponse {credits_charged: 450, estimated_frame_cost: 450, id: "clx7uu86w0a5qp55yxz315r6r".to_string()}

Notice what makes this LLM-friendly:

Clear Structure: Structure helps models understand what your content is and when to surface it. Even if indexed, a page may be skipped if meaning isn't clear or the layout is hard to parse. Every parameter is clearly defined with descriptions and examples.

Semantic Richness: Explains what each parameter does, when it's required, and provide concrete examples. This gives LLMs the context they need to understand when to recommend this SDK.

Complete Examples: A working example that developers can copy and modify. When an LLM encounters this, it has everything needed to help a developer get started.

Contextual Information: Helps LLMs understand not just the syntax, but the business logic behind the API.

Why This Matters for Your Business

You're not just optimizing for humans. You're also optimizing for models that decide what humans see. That means going deeper, being clearer, and creating content that models can learn from and surface.

When developers ask ChatGPT "How do I generate videos from images in Rust?", Magic Hour's SDK shows up because:

1. The documentation is semantically clear - LLMs understand what the SDK does

2. The examples are complete - there's enough context for the LLM to provide helpful guidance

3. The structure is parseable - headers, tables, and code blocks make the content machine-readable

4. The intent is obvious - it's clear this is an SDK for video generation, not just random code

Compare this to poorly documented SDKs that just list function signatures without context. When an LLM encounters sparse documentation, it can't confidently recommend that solution. The developer never hears about your API.

The Competitive Advantage

Here's the thing most companies miss: LLMs benefit from content that covers multiple angles or uses different terms around the same topic. But they also favor content that's authoritative and complete.

A well-documented SDK does both. It covers the technical implementation (code examples), the business context (what problems it solves), and the practical usage (how to integrate it). This comprehensive coverage makes it more likely that LLMs will surface your SDK when developers are looking for solutions.

Sideko creates the kind of documentation that LLMs can understand and recommend. Every endpoint gets:

• Clear descriptions in natural language

• Complete parameter documentation

• Working code examples

• Context about when and why to use each feature

• Structured markup that's easy for machines to parse