How to Use AI Assistants for Tenjin SDK Integration: A Developer’s Guide

As mobile developers, we have grown comfortable leaning on AI assistants. You open Claude, ChatGPT, or GitHub Copilot, describe what you want to build, and within seconds you have working code. But that convenience comes with a hidden cost: hallucination.

Here’s the problem. When you ask an LLM to integrate a mobile SDK, you are asking it to rely on training data that may be months or years old. The SDK you are targeting may have evolved significantly since the model’s knowledge cutoff. APIs have changed, methods have been deprecated, and new patterns have emerged. The AI, however, doesn’t know this. It confidently hands you the code that looks reasonable but is fundamentally incompatible with the current SDK version. 

This is the core challenge we’re tackling head-on. Integrating Tenjin, or any rapidly evolving SDK, through an AI assistant shouldn’t mean debugging a phantom method or chasing depreciated calls. That’s why we built a better way.

The Reality: LLMs and SDKs Don’t Mix Well

Let’s ground this in a concrete scenario. 

You’re starting a new React Native project and want to integrate Tenjin for analytics and attribution. You open Claude and ask a straightforward question: “Add the Tenjin SDK to my React Native project.”

Drawing from its training data, which might include Tenjin SDK version 1.4.0 from early 2024, it generates something like this: 

import { NativeModules } from 'react-native';
const Tenjin = NativeModules.Tenjin;
Tenjin.initialize('YOUR_API_KEY');
Tenjin.connect();

import { NativeModules } from 'react-native';
const Tenjin = NativeModules.Tenjin;
Tenjin.initialize('YOUR_API_KEY');
Tenjin.connect();

Looks reasonable, but there’s a catch. The current version of the React Native Tenjin SDK (let’s say 1.8.0) shipped breaking changes. The initialize method now requires an object with specific configuration parameters, so the simple string-based approach no longer works. Your code compiles just fine, but at runtime, initialization fails silently. You might burn hours debugging, never realizing the SDK never actually initialized.

The same trap exists on Android. Ask an LLM to initialize how Tenjin and it might suggest the deprecated tenjinSDK.init()method. This is the one replaced by a builder pattern back in version 1.17.0. The code looks correct, it can build successfully, but when you test it, nothing gets tracked because the initialization call is dead code. 

This isn’t a failure of the AI, but rather a structural limitation. The LLM has no real time knowledge of Tenjin most up-to-date of Tenjin’s current API surface. It can’t know about this month’s release notes, let alone future changes. The model is performing exactly how it’s trained: pattern matching against its corpus and producing code that superficially resembles the correct code.

You can close this gap by using an AI Assistant to integrate an SDK.

The Solution: Structured AI Assistant Guides

Tenjin saw this problem coming and started publishing AI Assistant Guides for our SDK called LLM SDK Guides.  

• What is an AI Assistant Guide?
  An AI Assistant Guide is documentation (often called skills) written for language models that gives an AI assistant a machine-readable source of truth. It could contain API signatures, integration rules, and known pitfalls. 

These aren’t designed for humans, they’re instructions built to be consumed by language models. They are packed with explicit rules, current API signatures, and the anti-patterns to avoid. Most critically, they’re versioned alongside the SDK itself, so they always reflect the current version. No drift. No guess work. 

Each Tenjin LLM SDK Guide includes: 

When you feed one of these guides to an LLM before asking for help, the model’s context shifts. Instead of leaning on stale training data, it now has an authoritative source material to draw from. 

While hallucinations don’t disappear entirely,they become significantly less likely. The LLM is no longer interpolating between conflicting training examples; it has a source of truth.

This makes the difference between code that looks right and code that is right.

How to Use an AI Assistant: Tenjin’s LLM SDK Guide

Here’s a real workflow that uses Tenjin’s LLM Guide into the SDK integration process.

You’ve just created a new React Native project and you want to add Tenjin analytics. While the traditional move might be to skim the README or ask your AI Assistant directly, you want to be more intentional.

Step 1: Prepare the LLM SDK Guide 

Before you even open Claude (or similar), fetch the platform-detection guide from Tenjin’s repository.This is the entry guide that gives the assistant instructions. 

Step 2: Give Claude or Other AI Assistant Your Project Context 

Open Claude and paste your project structure and dependencies. Based on the guide, Claude identifies the correct platform-specific guide to use for your setup. 

Step 3: Provide Platform-Specific Guidance

At this stage, Claude requests or you proactively provide the platform-specific guide. Claude reads it, absorbing  the exact current method signatures and initialization code for your platform.

Step 4: Let Claude Generate Code for Integration

With the LLM SDK Guide as a reference, Claude generates your integration code. It doesn’t hallucinate an old API. With a guide, it won’tt invent methods that don’t exist.

Claude and other AI Assistants can now: 

• Explain why this is the correct approach 
• Reference the guide to justify each step 
• Warn you about the common mistakes

The result is an integration code you can trust. It’s grounded in the most current SDK and scope, not guesswork.

Example: Using the SDK Integration Guide with Dependency Injection

Here’s a concrete example for Android, on how to integrate Tenjin alongside your existing architecture: 

Suppose you’re building an Android app with Kotlin, Dagger2 for dependency injection, and you want to initialize Tenjin as part of your application startup. Armed with the LLM SDK integration guide, Claude can provide the most current information and tell you which Gradle dependency to add. It even shows you the exact initialization sequence to follow. 

Catching Subtle Bugs with the SDK Guide

One of the hidden benefits of using an AI Assistant Guide is catching subtle integration bugs. n When it comes to event tracking, the difference between passing a String and an Int is a small but critical error. When you reference an AI Assistant Guide, Claude knows exactly which method signature is correct and generates code that works accurately.

Adding Your Own Context to AI Assistant Guides

AI Assistant Guides are powerful, yet generic by design since they are made to work for any project. Because every project has its own specifications, context, and scope, it becomes something you’ll want to layer it in. 

How? Simply ask Claude how to integrate Tenjin alongside your current setup and services, or how to optimize initialization to your specific requirements. You get the best of both worlds: an authoritative source of truth and one that is tailored to your codebase. 

How to Use the SDK Guide for Debugging

The SDK integration guide isn’t just for building. It also becomes invaluable when integration goes wrong.

Instead of guessing, you point Claude at the guide’s testing checklist and verification section. With a manual reference in hand, Claude can flag common setup mistakes and walk you through the verification steps methodically, until you discover exactly where things broke. 

The Shift Towards AI-Ready SDKs

Tenjin isn’t the only one rethinking documentation for the age of AI. RevenueCat publishes AI-focused guides so LLMs get their subscription SDKs right. OneSignal maintains SDK AI prompts to keep models from hallucinating push notification APIs. The smartest teams within the space have all noticed that documentation now has two audiences: one of them isn’t human. 

It changes everything. For years, “good documentation” meant docs that humans could follow. Now a well-maintained library has to speak fluent markdown, communicating to the AI Assistants writing half the integration code out there. Although README files and tutorials aren’t going anywhere, they no longer have a main role in the story. 

Within a year, AI Assistant Guides will be a first class deliverable, shipped right alongside every SDK, documentation, and code samples. 

Key Takeaways

The main takeaway is simple: never trust an LLM to integrate an SDK without current reference material. The model’s training data is stale by default, and the cost of  outdated API usage is rarely obvious. It’s usually the kind of bug that compiles cleanly, falls silently, and wastes hours. Tenjin’s LLM guides are not optional documentation, but have become an essential tool in your integration workflow.

Before asking an AI assistant to help with Tenjin, do these three things: 

1. Fetch and present the guide.

This single step dramatically reduces hallucinations and shifts the model from guessing to reasoning from an authoritative source. 

2. Layer in your own projct-specific context.

The guide handles the SDK, while you bring the architectural constraints, performance needs, and integration points that the overall project specifications and scope. 

3. Verify the output against the guide, every single time.

Even when you trust the LLM, a quick check against your references and source saves hours of debugging and troubleshooting. 

The future of developer tooling is becoming clearer and clearer: humans write the rules, machines follow them, and developers benefit from both.

At Tenjin, we’re getting a head start and leading by example. Here you can find a skills file that you can use alongside your AI Assistant to Integrate our SDK.

This article was written by Enrique López-Mañas, Senior SDK Engineer at Tenjin.