Boosting iOS Development Efficiency With AI: Practical Techniques for a Large-Scale Codebase

Authored by: Salty (Taewoo) Kang

At Tinder®, we’ve been using various AI-powered tools to enhance developer productivity and automate repetitive tasks. Our iOS app runs on a massive codebase accumulated over many years, encompassing numerous interwoven features. The project is composed of a large number of modules, a top-level app target, individual modules, and domain-specific example app targets, resulting in many considerations during development and debugging.

Within this structure, developers must manage complex UI and business state flows, where even minor changes can trigger unexpected side effects, and debugging often comes at a high cost. As a result, repetition and complexity can easily scatter developers’ focus and hinder productivity. In response, we set out to explore how AI tools could meaningfully enhance productivity and integrate naturally into our workflows.

In this post, we'll share the concrete prompting strategies and techniques that are effective for iOS development at scale. These approaches can be adapted to your specific development environment and constraints.

Teaching AI through examples: Reference commits

AI-assisted development works best when it can learn from your previous work to complete similar tasks consistently. When dealing with complex codebases, natural language prompts alone often fail to deliver the precision and consistency required for large-scale refactoring.

During repetitive refactoring tasks that span multiple files, AI agents often misinterpret initial prompts or deviate from the intended pattern. This becomes problematic when the transformation involves nuanced architectural decisions that aren't easily captured in prose.

The analytics migration challenge

Consider a real-world scenario: migrating analytics systems across multiple user interface screens. The Edit Profile screen is a key interface where users can update their information, such as School, Gender, or Relationship Goals.

We've been collecting analytical data from these interactions, and recently, we initiated a large-scale migration to a new metrics system.

Below is a simplified version of our original structure:

(Please note that the example code below has been modified from the actual implementation for clarity and illustrative purposes.)

Migrating to the new metrics system required not just replacing event calls, but also deeper structural refactoring:

• Isolate legacy events so they can be safely removed later with no side effects.
• Apply the new analytics API.
• Build abstractions with dependency injection to ensure testability.

Here’s the outcome we aimed for:

The conventional prompting approach

To instruct an AI agent to perform this refactor, a conventional approach would involve detailed natural language instructions:

"In SchoolEditActivity, split the analytics code into legacy and new systems. Replace the existing analytics property with legacyAnalytics and newAnalytics. Create separate implementations for each, and update the constructor to inject both. Ensure all dependencies are injected appropriately. Call both analytics systems on every event, keeping the existing logic intact."

Despite providing such detailed prompts, AI agents frequently misinterpret the instructions or miss subtle implementation details. When applying similar treatment to other screens (like CityActivity or JobActivity), developers find themselves repeatedly crafting lengthy prompts with inconsistent results.

The commit reference technique

Rather than relying on natural language descriptions, referencing concrete git commits as examples produces better accuracy. After updating a simpler screen called BioEditActivity, the prompt becomes:

Reference [commit 1 hash] [commit 2 hash] → apply similar changes to SchoolEditActivity.swift and its related files

This approach leverages the AI agent's ability to analyze actual code changes rather than interpreting abstract instructions. The agent can examine the commit's diff to understand structural patterns (how classes were refactored and dependencies reorganized), implementation details (specific method signatures and parameter handling), testing approaches (how the changes were validated), and naming conventions (consistent variable and protocol naming).

Why commits work better than prompts

Git commits represent concrete, executable examples of the desired transformation. They eliminate ambiguity by showing exactly what changed, not just what should change. For large-scale refactoring work, this approach delivers higher accuracy (AI agents replicate patterns more reliably than interpreting descriptions), faster iteration (single prompts replace multiple correction cycles), and better consistency (the same transformation pattern applies uniformly across files).

Git commits have evolved beyond version history. They now serve as effective training data for AI-assisted development.

Autonomous build-error-fix loops

AI agents can also streamline the traditional build-debug cycle that consumes substantial developer time in iOS projects.

The hypothesis: What if AI could build the code, analyze compilation errors, and fix them autonomously?

The context switching problem

Traditional iOS development workflows with AI agents create frequent context switching between AI editing development environments and Xcode. After writing or editing code, developers typically must:

1. Build the project using Xcode
2. Copy error messages from build output
3. Context switch back to the AI agent and paste the messages to be fixed
4. Repeat the cycle until compilation succeeds

This constant context switching fragments focus and reduces development velocity, especially during large refactoring efforts where compilation errors cascade across multiple files.

Implementing autonomous build loops

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The solution involves configuring AI agents to execute build commands directly and parse their output for actionable error information. This creates an autonomous loop:

Code change → Auto build → Parse CLI output → Detect error → Fix → Rebuild

Most agentic AI tools support command execution capabilities that can be configured for safe autonomous operation. The key setup requirements include command execution permissions (enable the AI agent to run build commands autonomously), command whitelisting (restrict automatic execution to safe commands like build scripts, while requiring confirmation for potentially dangerous operations), and build output parsing such as logs.

This typically involves whitelisting commands such as:

• Custom build scripts (e.g., build.sh)
• Git commands (e.g, git show, git log)
• Test execution commands
• Linting and static analysis tools

Practical implementation example

Consider a common scenario where uncommented code causes compilation failure:

With conventional workflows, developers discover this error only after manually building in Xcode and parsing build logs. The autonomous approach eliminates this friction entirely.

The developer simply prompts: build it and fix if error as needed.

The AI agent then:

1. Executes the build command and captures output
2. Parses compilation errors to identify the problematic line
3. Applies the fix (commenting out the invalid syntax)
4. Rebuilds automatically to verify the solution
5. Reports success or continues the cycle if additional errors exist

High-impact Use Cases

This autonomous build-fix loop enables effective handling of various scenarios. When method signatures change, the loop automatically verifies and updates related call sites. It also maintains and repairs broken test code after refactoring, using the loop to apply and validate fixes automatically.

Performance Characteristics

AI agents achieve high accuracy by relying on actual compiler output rather than assumptions. This allows developers to stay focused on architecture and business logic, as minor compilation errors are resolved quickly without breaking the development flow.

However, several limitations remain. Complex errors still require developer intervention. AI agents may misinterpret context, applying fixes that resolve compilation issues but introduce logical errors. Additionally, defining safe command boundaries can be challenging and requires experience and careful judgment.

Persistent guidance: Eliminating repetitive prompting

The autonomous build-fix loop proved effective, but revealed a persistent friction point. In development environments where teams use custom build scripts instead of standard tools like xcodebuild, engineers had to repeatedly provide contextual instructions such as these to iteratively unblock the AI agent. Like:

"Do not use xcodebuild. We use a custom build script."

"Remember, our coding standards require depending on abstractions rather than concrete types."

"Always run tests after making changes to core logic."

This repetitive prompting creates overhead and introduces inconsistency when team members provide different guidance for the same scenarios.

Guiding principles and behavioral rules

This solution defines persistent guiding principles and behavioral rules to ensure consistent AI agent behavior across sessions. As a result, developers don’t need to repeat the same instructions every time. Instead of relying on ad hoc prompts, teams can externalize their expectations as reusable, structured guidelines.

For example, guidance such as using a custom build command or adhering to a specific coding style can be stored in a text file within the project. Examples are:

These files are automatically referenced when prompts are executed, allowing the AI to act accordingly without the developer having to explicitly enter instructions each time.

While the implementation details may vary depending on the tools and platforms being used, the core idea remains the same:

Teams define clear internal guidelines, store them in a file, and enable the AI agent to refer to that file in order to apply consistent instructions. The AI should operate according to a set of principles tailored to the team’s development context. These principles may cover tooling preferences, architectural decisions, testing strategies, and coding conventions.

Rather than specifying behavior manually in every interaction, setting these principles once and applying them consistently helps reduce friction and improve alignment between human intent and AI-driven workflows.

Code comprehension and analysis techniques

Navigating complex architectures

Another challenge that a lot of engineers face was exploring unfamiliar domains in massive codebases. ViewModels, coordinators, and services can quickly overwhelm developers when onboarding to new projects or investigating unfamiliar subsystems.

AI agents excel at generating structural summaries and visual representations of complex code relationships on demand.

Effective prompting patterns include:

• "Generate a flowchart showing the data flow in the edit profile system"
• "Summarize the relationships between ViewModels and their corresponding services"
• "Explain the architecture in this module"

These prompts leverage the AI agent's ability to analyze multiple files simultaneously and extract architectural patterns that might not be immediately apparent to developers working within individual components.

Performance Analysis and Debugging

AI agents demonstrate particular strength in parsing and interpreting performance data from iOS debugging tools. Traditionally, developers had to manually read through logs or visually analyze graphs in tools like Instruments to identify performance bottlenecks. This process was often time-consuming, error-prone, and required deep familiarity with profiling tools.

By contrast, AI agents can dramatically streamline this process by analyzing raw performance data directly and surfacing issues that may be subtle or easily overlooked. When provided with performance logs such as OSSignPost traces, AI agents can extract temporal data (e.g., durations, event frequencies, timing intervals), correlate metrics with code paths to identify bottlenecks, and recommend actionable optimizations. For instance, an agent might detect that a specific logic block is blocking the main thread for several hundred milliseconds and suggest moving that work to a background queue.

One recommended workflow is to extract performance data from metrics tools such as Instruments, feed the raw data into the AI agent, and request analysis to gain insights into logic-level bottlenecks. Even when such logs are not initially available, AI agents can assist by generating logging code to collect the necessary data. Here’s a hypothetical example.A developer is implementing a messaging logic like:

And ask to AI:

“Add signpost logging at each function call to measure the execution time of each function during this message-sending process.”

In that case, the AI analyzes the logic and adds logs as shown below, so developers don’t have to enter them one by one, which is more convenient.

Once the logging is added and the app is run, the collected data can be analyzed using the same process.

In addition, developers can also provide charts or graphs as screenshots to the AI agent for analysis. For example, when encountering a memory leak caused by a capturing issue during iOS development, sharing a screenshot of the object graph from Xcode’s memory graph where the capture occurs allows the AI agent to identify potential retain cycles, suggest code-level fixes, or confirm whether the observed pattern matches a typical memory leak scenario. This workflow significantly reduces the cognitive load of manually interpreting graphs and accelerates root-cause analysis.

This approach not only shortens the time needed to derive meaningful insights from complex performance data, but also introduces a new layer of perspective, surfacing subtle performance risks that may be missed through manual analysis.

Conclusion

The techniques discussed here aren’t grand theories or futuristic ideas.

They’re practical methods that are applicable in day-to-day iOS development. Imperfect, but worth using again and again. Rather than treating AI as an experimental novelty, we’ve found it more valuable to see it as a practical assistant that fits naturally into the development workflow.

Here are some key takeaways from our experience:

• Commit-based prompting helps turn vague requests into concrete, actionable code changes. Instead of relying on generic instructions, using actual commit diffs to express intent leads to greater precision and consistency.
• Autonomous build-fix loops allow us to fix errors without breaking focus. In particular, being able to edit and rerun code without switching between multiple applications like Xcode or the terminal helps maintain momentum.
• By defining persistent behavioral rules and team guidelines, we avoid repeating the same instructions and ensure that the agent operates in line with our technical and architectural standards.
• The agent can also analyze code structure or performance data and identify bottlenecks or inefficiencies that might not be immediately obvious to a human developer. This has proven especially useful when working with unfamiliar codebases or tuning performance in a project.

In energy-draining tasks like tedious tasks, refactoring, or performance analysis, the AI agent lightens the load. Sure, it occasionally makes irrelevant suggestions or generates overly complex solutions. However, I or our engineering team always stays true to the same philosophy:

“If it works, great. If not, I’ll fix it.”

While the agent takes care of one task, I focus on another. That kind of lightweight task distribution adds up, over time, it affects both productivity and developer fatigue.

AI isn’t a silver bullet. But used well, it absolutely gives back what you put into it.