Building an SDK developers love: Best practices for AI integrations and customization

Introduction: the developer's dilemma

Imagine trying to build an SDK flexible enough to meet every developer’s needs without making integration a nightmare. It’s a balancing act that many teams struggle with.

When designing the Beefree SDK for enterprise customers, we faced this challenge head-on. Developers needed deep customization, but they also wanted a frictionless setup. Striking that balance meant making tough decisions about extensibility, usability, and modular design.

In this article, we’ll explore key best practices for designing a highly customizable SDK, backed by lessons we learned while building the Custom LLM AddOn, a feature that lets developers integrate their own AI models into our platform.

By the end, you will have actionable strategies for making your SDKs developer-friendly, extensible, and scalable without overwhelming your users.

Five core principles for building a developer-friendly SDK

When designing an SDK, flexibility alone isn’t enough. You need scalable and intuitive customization options that make integration seamless. Here’s what we learned.

1. Enable two-way communication

A great SDK doesn’t just expose an API. It facilitates communication between the SDK and the host application.

For example, Beefree SDK uses:

• Content Dialog interfaces to enable seamless interaction between the editor and external tools.
• Callbacks that let developers react dynamically to user actions and system events inside the builder.

These mechanisms give developers full control over their implementation while ensuring smooth communication with the SDK.

Ask yourself: Where can you introduce better two-way communication in your SDK?

2. Balance customization and simplicity

Developers fall into two camps:

• Some want ready-to-use tools that work out of the box.
• Others need fully customizable options to match their workflows.

A well-designed SDK should cater to both.

At Beefree, we provide:

• Pre-built solutions that require minimal setup, like our AI Writing Assistant.
• Extensible interfaces that let developers customize their users’ experience, for example by plugging in their own AI models via the Custom LLM AddOn.

By offering both options, we keep integrations simple while allowing full flexibility when needed.

Evaluate your SDK: Are there areas where customers struggle with excessive complexity?

3. Start small, iterate, and scale

Instead of over-engineering every feature from the start, launch with a minimum viable product (MVP) and iterate based on developer feedback.

For the Custom LLM AddOn, we initially:

• Focused only on AI text generation instead of tackling image generation, alt-text, and translations all at once.
• Released a basic interface that we could expand as needed.

This approach saved development time and ensured we were building what developers actually wanted.

Challenge: What’s the simplest version of your next feature that still delivers value?

4. Involve customers early

One of the biggest mistakes SDK teams make is building in isolation.

When working on the implementation of the Custom LLM AddOn, we engaged enterprise customers before development even started. Their feedback helped us:

• Discover missing features we hadn’t considered.
• Define the right balance between flexibility and usability.
• Ensure seamless integration with existing enterprise workflows.

Try this: Identify one feature where early customer input could have prevented redesigns.

5. Treat documentation as a core feature

An SDK is only as good as its documentation. Even the most powerful feature is useless if developers can’t figure out how to implement it.

Our "Us First" principle means we are our first customer. This applies to documentation as well. For example, before launch, we:

• Have someone on our team who didn’t build the feature try to implement it using only the documentation.
• Fix unclear areas before customers hit roadblocks.

Test your docs: Can a new developer implement a feature without internal help? If not, fix the documentation before launch.

Case study: how we built a Custom LLM AddOn

The problem: enterprise AI adoption barriers

While AI-powered features are exciting and highly requested, enterprise clients were hesitant to use our AI Writing Assistant because:

1. They had strict data privacy concerns about third-party models like OpenAI.
2. Many already had their own custom LLMs fine-tuned for their business.

To bridge the gap, we needed a Custom LLM AddOn that would allow them to seamlessly integrate their own models without requiring them to rebuild every AI-powered module from scratch.

Our solution: a flexible AI integration framework

We applied the five core principles explained earlier to solve the problem.

• Two-way communication
  We used the content-dialog interface to allow real-time interaction between the SDK and the customer’s AI model.
• Early customer collaboration
  Enterprise customers helped define which inputs and outputs were essential for integration.
• Customization vs. simplicity
  Instead of enforcing a prebuilt UI, we gave developers full control to design an experience that matched their product.
• Start small and iterate
  Focused first on text generation before expanding to other AI-powered features.
• Documentation-first approach
  Created developer docs early, ensuring seamless adoption and allowing for early feedback from our customers.

By focusing on developer experience, we delivered a powerful, extensible AI integration without adding unnecessary complexity.

Final thoughts: what makes an SDK developer-friendly?

If you’re building an SDK that developers love, focus on these key takeaways:

• Enable seamless two-way communication to let the SDK and host application interact dynamically.
• Balance out-of-the-box simplicity with deep customization to accommodate different developer needs.
• Start small and iterate instead of overbuilding before validating real demand.
• Engage customers early to avoid wasted development effort.
• Treat documentation as a core feature rather than an afterthought.

If you’re designing an SDK, take a step back and evaluate:
✅ Are you giving developers the flexibility they need without overcomplicating integration?
✅ Is your SDK intuitive enough to get up and running quickly?
✅ Are you treating documentation as seriously as the code itself?

These principles shaped the way we built the Beefree SDK, and we’ve seen firsthand how they help developers ship faster with fewer roadblocks. Applying these lessons can make a huge difference in adoption and usability if you're building customizable SDKs.

Want to see how Beefree SDK handles AI customization? Explore our documentation or reach out to our team.

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