When AI Meets Unfamiliar Third-Party Libraries: A Source-Driven Technical Research and Implementation Workflow

AI-assisted development has evolved from “optional” to “default configuration.” Writing business logic, adding unit tests, debugging errors—our first reaction is often to ask large models.

But as long as you’ve done slightly complex engineering integration, you’ve definitely encountered these situations:

The model gives mismatched dependency versions, and you can’t find the APIs in the code it suggests;
Its examples can run, but they’re completely different from the version used in your project;
It speaks confidently, but you can’t tell if it’s correct or just “hallucinating.”

Especially when facing unfamiliar, rapidly evolving third-party libraries, this uncertainty is magnified exponentially.

This article organizes a workflow I’ve repeatedly validated: a Source-Driven AI research and integration method.

It’s not about “using AI less,” but about using AI without being misled by AI. The goal is: regardless of how new the library is or how poor its documentation, you can quickly build reliable understanding and implement it stably.

1. Why Do Large Models Fail at Third-Party Library Integration?

Let’s start with the conclusion: AI is not good at answering third-party library questions that are “detached from specific versions and contexts.”

The reasons are typically not poor model capability, but inherently unstable information chains:

1.1 Knowledge Cutoff

Large model training data has time boundaries, and new libraries or versions are often not in memory.

1.2 Rapid Version Iteration

Popular libraries’ APIs frequently undergo breaking changes, making blog posts and answers instantly outdated.

1.3 Unreliable Documentation

READMEs/Wikis may be missing, lagging, or even inconsistent with source code.

1.4 Complex Context

Library usage often depends on platforms, build tools, experimental flags, or implicit constraints. Without context, the model can only “complete.”

The phenomenon we see is: models are good at “explaining,” but not necessarily “runnable.”

2. Solution Approach: Make Source Code the Ground Truth for AI

The core tenet of this method is just one sentence:

Trust the source, not the memory. Trust the source, not the memory.

In other words:

AI’s memory is “potentially outdated priors”
Source code is “current version facts”

You shouldn’t directly ask: > “How do I use this library?”

Instead, have AI answer based on first-hand materials: > “Based on this source code, how should this library be used?”

When you treat source code as AI’s context, the reliability of its answers changes qualitatively.

3. Source-Driven Workflow (Four Steps)

Step 1: Obtain First-Hand Materials

Don’t ask the model right away. First, grasp the library’s “reality” in your own hands.

Include at minimum:

Repository source code
build / gradle / package configuration
sample / demo
release / changelog (if available)

The operation is simple:

git clone <repo-url> /tmp/lib-analysis
cd /tmp/lib-analysis

# Check overall structure
ls
# Check dependencies and version information
cat build.gradle.kts
# Look for examples
ls sample demo examples

At this stage, you can already answer several key questions:

Which is the latest version? When was the last commit/release?
How active is maintenance? You’ll get a feel just by looking at the timeline
Are there many breaking changes? The changelog can quickly reveal this

This step isn’t formalism—it directly determines the “foundation” of your subsequent understanding.

Step 2: Build a Knowledge Map

When reading source code, there are two things to fear most:

Diving in blindly and spending half a day without finding the core
Details everywhere, ending up with only the illusion of “having read it”

So the second step is to: first build a global cognitive framework, pinning the library’s core concepts on the wall.

You can build a knowledge map with this structure:

What problems does the library solve?
What are the core abstractions/models?
How are modules layered?
What are the capability boundaries? (supported/not supported)
What is the recommended usage path?

This step doesn’t pursue details, only direction.

Its value lies in:

You know which files are key to read
You can have clear goals when talking with AI
The final output documentation/blog is naturally structured

Step 3: Grasp Core Implementation for Deep Dive

With a knowledge map, you can “make precise cuts.”

Prioritize three types of files:

Core State / Data Models These determine how the library’s state flows and how capabilities are expressed.
Core Entry / Facade APIs These are the interfaces users will actually encounter.
Sample Code / Samples They usually reflect how the author truly wants you to use it.

Don’t be afraid of “slowness” when diving deep. True understanding comes from being able to answer:

Why was it designed this way?
What are the trade-offs behind this code?
If I want to extend/refactor it, what should I be most afraid of breaking?

When you can clearly explain these, you’re no longer just “someone who uses the library,” but “someone who understands the library.”

Step 4: Experimental Validation (Turn Inference into Fact)

The final step must be done: write small experiments to run your understanding.

The reasons are practical:

You might have misunderstood (especially complex state machines)
You might have missed hidden constraints (experimental APIs, platform limitations, initialization dependencies)

Experiments don’t need to be large, just cover key paths:

@Composable
fun MinimalTest() {
    val state = rememberXXXState()

    LaunchedEffect(Unit) {
        // 1. Initialization path
        state.init()

        // 2. Core capabilities
        state.doSomething()

        // 3. Boundary capabilities
        state.undo()
        state.redo()
    }
}

When the experiment runs successfully, what you get isn’t “feels right,” but “factually established.”

4. Why Can This Method Significantly Reduce AI Hallucinations?

Because it changes how AI works.

Before: AI finds answers in its “memory library”
Now: AI finds answers in “the source code context you provide”

Thus:

It doesn’t have to guess versions
It can infer APIs based on real code
Every step of its answer is traceable

You also shift from “passive trial and error” to “active validation.”

AI is no longer the source of truth, but your analysis accelerator.

5. Making the Workflow Reusable with a Checklist

To avoid “reinventing the wheel every time,” I suggest you solidify the analysis process into a team-level convention:

Before Analysis

Latest version / release time?
Is maintenance active?
Any recent breaking changes?

During Analysis

What are the core data/state models?
What are the main entry APIs?
What best practices do sample usages reflect?
Which APIs are experimental or have platform limitations?
How are performance/caching/threading handled?

Output

Minimal runnable example
Capability list + boundary descriptions
Architecture and data flow summary
Common pitfalls / debug entry points
Implementation suggestions adapted to your project

The significance of this checklist is: turning research from “experience-based” to “process-based.”

6. Conclusion: Turn “Unfamiliar” into Your Advantage

In the future, you’ll encounter more:

New libraries
New frameworks
New versions
Projects with missing documentation

Relying on search and model memory will only drag you down with version differences.

The source-driven workflow provides a stable path:

Get source code and examples
Build a knowledge map
Dive deep into core implementation
Experimental validation
Output reusable conclusions

Next time you encounter an unfamiliar library, don’t panic: trust the source first, then use AI. You’ll find that your speed and stability in integrating any library significantly improve.

Appendix: Practical Cases

Case 1: Rapidly Iterating UI Library

Once needed to integrate a UI component library. Documentation showed the latest version was 2.1.0, but cloning the repository revealed the main branch was already 3.0.0-alpha with significant API changes.

Through source code analysis, I discovered:

Core components changed from class to function components
Theme system was completely refactored
Some features were marked as deprecated

If I had integrated directly based on documentation, I would have encountered numerous compilation errors. But through the source-driven approach, I anticipated breaking changes in advance and chose the stable 2.1.0 version.

Case 2: Tool Library with Missing Documentation

A certain data processing library had only a simple README but rich internal functionality. Through analyzing the source code, I discovered:

The library uses a state machine-like design pattern
There are hidden performance optimization options
Certain API combinations cause memory leaks

None of this critical information was reflected in the documentation—only deep source code could reveal it.

The core value of this workflow lies in: making uncertainty controllable and AI collaboration reliable.

Hope this helps you! If you have similar experiences or other methods, welcome to discuss in the comments.