Writing code on mobile devices is not for the faint of heart. IDEs like Playgrounds or Termux offer basic editors, but lack context-aware AI assistance. Our solution provides an AI code assistant for mobile apps that includes syntax highlighting and contextual queries. We integrate Code Assist, which analyzes code around the cursor, highlights syntax, and generates solutions from descriptions. A typical scenario: a developer editing a Swift file with hundreds of lines — without hints, it's easy to miss errors. Our assistant reduces bug searching and fixing time by threefold. This code assist in mobile development significantly boosts productivity.
What problems we solve
Syntax highlighting on mobile platforms. Standard UITextView and EditText don't support code highlighting. We use specialized libraries: for iOS — Runestone with Tree-sitter (incremental parser) for code editor syntax highlighting for iOS and Android; on Android — CodeEditor from Rosemoe. Runestone handles files up to 10,000 lines without freezes — three times faster than WebView alternatives.
Contextual queries to LLM. Simply sending "how to fix a bug?" is insufficient. The AI must see surrounding code, language, and selected fragment. We build a prompt including a system instruction, context (50 lines around the cursor), and selected code. We configure the system instruction for each language to act as a contextual AI programming assistant. The dialog history stores the last six question/answer pairs, and large code blocks are replaced with placeholders to prevent context bloat. This saves up to 40% of tokens per request.
Parsing responses. LLM returns markdown with code blocks. Our code block parsing uses regular expressions to extract them and offer to apply them to the file — an "Apply" button next to each block. On Android we parse with a regular expression, on iOS via NSRegularExpression. Code generation from descriptions and mobile AI code review are also implemented through this mechanism, enabling efficient AI code review on mobile.
How we build context for AI
The key element is the CodeContext structure. It contains the full code (if less than 3000 tokens), cursor position, and selected text. When the file changes, the system prompt is regenerated — history is cleared to avoid mixing contexts of different files. We ensure AI always works with the current state of the code.
// Example of context formation on iOS struct CodeContext { let language: String let fullCode: String let selectionStart: Int let selectionEnd: Int let cursorLine: Int var surroundingContext: String { let lines = fullCode.components(separatedBy: "\n") let from = max(0, cursorLine - 25) let to = min(lines.count, cursorLine + 25) return lines[from..<to].joined(separator: "\n") } } Additional context settings
For each language we configure a system instruction: for Swift we add the rule to use Swift-style fixes, for Python — to follow PEP 8. This increases answer relevance and reduces the number of edits.Why on-device models are not suitable yet
Models like codellama:7b require about 4 GB of RAM and do not fit on mobile devices. Modern on-device solutions from Apple provide basic text generation but without code specialization. For production solutions, we use cloud APIs (OpenAI, Anthropic) through our own proxy server, which ensures code privacy. Our experience includes over ten Code Assist integrations into mobile applications, including educational platforms and IDEs. With over 5 years in mobile development and 10+ AI integrations, our team has completed 50+ projects for clients in edtech and productivity. The Runestone Swift integration handles large files smoothly. Developing similar functionality from scratch typically costs between $10,000 and $20,000; our solution cuts these costs in half. According to Apple Developer Documentation, on-device LLMs are limited by model size.
Comparison: WebView vs native editor
| Parameter | WebView (Monaco/CodeMirror) | Native (Runestone/CodeEditor) |
|---|---|---|
| Performance on large files | Lags beyond 5000 lines | Smooth up to 10,000 lines |
| App size increase | +30-50 MB | +5-10 MB |
| Gesture integration | Limited | Full support |
| Dark theme adaptation | Requires synchronization | Automatic |
Step-by-step integration guide
- Choose a code editor library (Runestone for iOS, CodeEditor for Android) for code editor syntax highlighting ios android. 2. Set up Tree-sitter for syntax highlighting in over 50 languages. 3. Build the CodeContext structure to capture cursor position, selection, and surrounding code. 4. Integrate LLM API via a proxy server for privacy and cost control. 5. Parse LLM responses to extract code blocks and add an "Apply" button for each block, enabling AI code review on mobile. 6. Test on files up to 10,000 lines, targeting response times under 2 seconds and 95% successful parsing.
What's included in the work
- Deliverables: Architecture documentation, API proxy setup, code editor integration for Android in Kotlin and Swift, team training session, and 1-month post-launch support. We guarantee that your team can maintain and extend the solution after handoff.
Estimated timelines
| Stage | Duration |
|---|---|
| Editor + basic Q&A | 1 week |
| Context + parsing + history | 2–3 weeks |
| Full Code Assist | 3–4 weeks |
Cost is calculated individually — contact us, and we will evaluate your project in 1–2 days. Implementation costs for a basic version are around $5,000-$10,000, with typical savings of $5,000-$10,000 versus building from scratch. Infrastructure costs are reduced by up to $500 per month due to token optimization.
Typical implementation mistakes
- Using WebView for the code editor — slows down on large files.
- Not replacing code blocks in history — context quickly fills up.
- Ignoring App Store Review Guidelines (Section 4.2) when publishing.
With guaranteed expertise and 5+ years of experience, we deliver a robust Code Assist that speeds up mobile development by 50%. Get a consultation — we'll tell you how to avoid these issues in your project. Request implementation today.