+
+
+
+ {{ content }}
+
+
+ {% include footer.html %}
+
+
\ No newline at end of file
diff --git a/_layouts/post.html b/_layouts/post.html
new file mode 100644
index 0000000..892a820
--- /dev/null
+++ b/_layouts/post.html
@@ -0,0 +1,25 @@
+---
+layout: default
+---
+
+
+
+ {{ content }}
+
+
+
+
\ No newline at end of file
diff --git a/_posts/2025-04-11-ides-to-ai-agents.md b/_posts/2025-04-11-ides-to-ai-agents.md
new file mode 100644
index 0000000..818a1a0
--- /dev/null
+++ b/_posts/2025-04-11-ides-to-ai-agents.md
@@ -0,0 +1,153 @@
+---
+layout: post
+title: "The Evolution of Software Engineering: From IDEs to AI Agents"
+date: 2024-04-11
+categories: [ai-assisted-development]
+read_time: 6 min
+permalink: /blog/ides-to-ai-agents
+image: /branding/posts/ide-to-ai-agents.png
+excerpt: "Exploring how software development has transformed from manual coding to AI-assisted development, and what the future holds for the industry"
+---
+
+
+
+ April 11, 2025
+ AI-Assisted Development
+
+
The Evolution of Software Engineering: From IDEs to AI Agents
+
Exploring how software development has transformed from manual coding to AI-assisted development, and what the future holds for the industry.
+
+
+
+ Katie Stoltz
+ Software Developer
+
+
+
+
+
+
+
+
+
+
Software engineering has undergone a remarkable transformation over the past few decades. What once required manual coding in basic text editors has evolved into sophisticated workflows powered by artificial intelligence.
+
+
The Manual Era: IDEs and Online Resources
+
Not too long ago, the typical software engineering workflow looked quite different from today. Developers would spend their days in Integrated Development Environments (IDEs) like Visual Studio, Eclipse, or IntelliJ, writing code line by line while constantly switching to browser tabs filled with Stack Overflow questions, documentation pages, and tutorial blogs.
+
+
This workflow was functional but inefficient. When faced with a coding challenge, engineers would:
+
+
+
Recognize a problem
+
Formulate a search query
+
Sift through multiple search results
+
Read through threads and documentation
+
Adapt found solutions to their specific context
+
Implement and test the solution
+
+
+
The process was time-consuming. A seemingly simple task like implementing a specific API call or fixing a particular bug could consume hours as developers hunted for the right answer amidst a sea of information.
+
+
The Search Engine Era: LLMs as Knowledge Tools
+
The introduction of Large Language Models (LLMs) like ChatGPT, Claude, and others around 2022-2023 marked the first major shift in this paradigm. These models could synthesize information across vast knowledge bases and present solutions directly to the programmer.
+
+
Initially, developers used these tools primarily as enhanced search engines. Rather than combing through multiple Stack Overflow threads, they could ask an LLM, "How do I implement JWT authentication in Express.js?" and receive a comprehensive answer in seconds.
+
+
The time savings were significant, but interestingly, they came more from reducing search time than from reducing actual coding time. Engineers still needed to understand the solutions, adapt them to their specific context, and implement them in their codebase.
+
+
The Agent Revolution: AI as Collaborative Partners
+
Now, we're entering a new era where AI isn't just answering questions but actively participating in the development process. These AI coding agents vary in capability and focus, creating a spectrum of assistance:
+
+
Copilot: The Autocomplete Agent
+
GitHub Copilot, developed by GitHub and OpenAI, was one of the first coding agents to gain widespread adoption. It functions primarily as an intelligent autocomplete system that:
+
+
Predicts and suggests code as you type
+
Completes functions based on comments or function signatures
+
Generates boilerplate code
+
Suggests implementations for common patterns
+
+
+
The primary benefit of Copilot is in reducing the cognitive load of writing code. By handling the routine aspects of coding, it allows developers to focus on higher-level problem-solving. Studies have shown that developers using Copilot can complete tasks up to 55% faster than those without it (GitHub, 2023).
+
+
However, Copilot's scope is generally limited to the current file or immediate context. It excels at line-by-line suggestions but lacks broader project awareness.
+
+
Cursor: The Complete and Commit Agent
+
Cursor takes the concept further by functioning as both an autocomplete and "commit" agent. Beyond suggesting individual lines of code, Cursor can:
+
+
Implement entire functions based on descriptions
+
Refactor existing code
+
Debug and fix issues
+
Explain code comprehensively
+
Generate test cases
+
+
+
The key advancement with Cursor is its ability to understand and manipulate larger chunks of code. Rather than suggesting individual lines, it can generate or modify entire functions or components.
+
+
This broader scope allows developers to work at a higher level of abstraction. Instead of dictating implementation details, engineers can focus on what they want the code to accomplish, allowing Cursor to handle more of the implementation details.
+
+
Devin: The Feature-Complete Agent
+
Devin, developed by Cognition, represents the cutting edge of AI coding agents. As an "autocomplete your feature" agent, Devin:
+
+
Understands entire codebases and their architecture
+
Implements complete features across multiple files
+
Writes tests and documentation
+
Debugs complex issues across the entire application
+
Plans implementation approaches
+
+
+
Where Copilot helps with individual lines and Cursor with functions, Devin operates at the feature level. Developers can describe a feature they want to implement, and Devin can handle the entire implementation process from planning to testing.
+
+
According to Cognition's research, Devin successfully completed 13.86 out of 14 tasks in a benchmark of real-world GitHub issues, demonstrating its ability to handle complex, multi-step development tasks (Cognition, 2024).
+
+
The Future of AI-Assisted Software Engineering
+
As these technologies continue to evolve, what might the future hold for software engineering?
+
+
The Evolution of Developer Roles
+
The role of software engineers is likely to shift up the abstraction ladder. Rather than focusing on implementation details, developers may become more like architects and product managers, defining what features should do and how they should integrate with the rest of the system.
+
+
This doesn't mean programming skills will become obsolete; quite the opposite. Understanding how code works, debugging complex issues, and evaluating the solutions proposed by AI will remain crucial skills. The difference is that developers will spend less time writing boilerplate and more time on creative problem-solving.
+
+
Collaborative Intelligence
+
The future likely isn't one where AI replaces developers but where each enhances the other's capabilities. AI agents can handle routine tasks, generate initial implementations, and suggest optimizations, while human developers provide the creative direction, quality control, and domain expertise.
+
+
This collaborative approach leverages the strengths of both human and artificial intelligence: humans excel at understanding context, setting goals, and making value judgments, while AI excels at pattern recognition, consistency, and handling repetitive tasks.
+
+
Personalized Agents
+
We may see the emergence of personalized AI coding agents that adapt to individual developers' styles, preferences, and workflows. These agents would learn from interactions with specific developers, becoming more effective partners over time.
+
+
Imagine an AI agent that not only knows programming languages but also understands your project's architecture, coding standards, and even your personal coding quirks.
+
+
Cross-Functional Agents
+
Future AI agents might expand beyond coding to interact with other aspects of the development process. An agent might help with:
+
+
Requirements gathering and refinement
+
User research and feedback analysis
+
Performance monitoring and optimization
+
Deployment and operations
+
+
+
This could create a more cohesive development experience where the boundaries between different phases of the software lifecycle become less distinct.
+
+
Conclusion
+
The evolution from manual coding to AI-assisted development represents a fundamental shift in how software is created. Each advancement from IDEs to LLMs to specialized agents has reduced friction in the development process and allowed engineers to focus on increasingly higher-level concerns.
+
+
As AI coding agents continue to evolve, the partnership between human creativity and machine efficiency promises to transform software engineering into a more productive and creative discipline. The future developer might spend less time wrestling with implementation details and more time exploring innovative solutions to complex problems.
+
+
The journey from IDEs and Stack Overflow to collaborative AI agents highlights an important truth: technology advances not by replacing human capabilities but by augmenting them, allowing us to achieve more than either humans or machines could accomplish alone.
+
+
References
+
+
GitHub. (2023). "GitHub Copilot Research Recaps: Productivity." Retrieved from https://github.blog/2023-10-30-research-quantifying-github-copilots-impact-on-code-quality/
+
Cognition. (2024). "Introducing Devin, the first AI software engineer." Retrieved from https://www.cognition.ai/blog/introducing-devin
+
Li, A., Liang, C., Shi, H., Wang, S., et al. (2023). "Large Language Models Can Self-Improve." Neural Information Processing Systems Conference. https://arxiv.org/abs/2210.11610
+
Barke, S., James, M.B., & Polikarpova, N. (2023). "Grounded Copilot: How Programmers Interact with Code-Generating Models." https://arxiv.org/abs/2206.15000
+
Cursor. (2024). "Cursor - The AI-first Code Editor." Retrieved from https://cursor.sh/
+
Börstler, J., Bennin, K.E., Hooshangi, S. et al. Developers talking about code quality. Empir Software Eng 28, 128 (2023). Retrieved from https://doi.org/10.1007/s10664-023-10381-0
+
+
+
+ AI
+ Software Engineering
+ Development Tools
+
+
\ No newline at end of file
diff --git a/_posts/2025-04-13-ai-pair-programming.md b/_posts/2025-04-13-ai-pair-programming.md
new file mode 100644
index 0000000..4c0bdcc
--- /dev/null
+++ b/_posts/2025-04-13-ai-pair-programming.md
@@ -0,0 +1,234 @@
+---
+layout: post
+title: "Hands-On with AI Pair Programming: A Developer's Experience with Cursor and Copilot"
+date: 2025-04-13
+read_time: 5 min
+categories: [ai-assisted-development]
+permalink: /blog/ai-pair-programming-cursor-copilot
+image: /branding/posts/hand-on-copilot-cursor.png
+excerpt: "A hands-on comparison of GitHub Copilot and Cursor, exploring their strengths and ideal use cases."
+---
+
+
+
+ April 13, 2025
+ AI-Assisted Development
+
+
Hands-On with AI Pair Programming: A Developer's Experience with Cursor and Copilot
+
A month-long experiment comparing two leading AI coding assistants on real-world projects.
+
+
+
+ Katie Stoltz
+ Software Developer
+
+
+
+
+
+
+
+
+
+
In my last post, I explored the evolution of software engineering from IDEs to sophisticated AI agents. This time, I'm getting hands-on with two of the most popular AI pair programming tools: GitHub Copilot and Cursor. For the past month, I've been using both tools across several real-world projects to understand their strengths, limitations, and ideal use cases.
+
+
The Experiment Setup
+
+
To provide a fair comparison, I designed an experiment to use both tools across three different project types:
+
+
+
API Service Development: Building a RESTful service with Node.js
Data Processing Script: Developing Python scripts for ETL operations
+
+
+
For each project, I spent one week using GitHub Copilot exclusively, followed by one week using Cursor.
+
+
GitHub Copilot: Strengths and Limitations
+
+
Where Copilot Excels
+
+
After extensive testing, I found that Copilot particularly shines in these scenarios:
+
+
+
Boilerplate Generation: Copilot is remarkably efficient at generating routine code patterns. When creating standard components like forms, API endpoints, or data models, Copilot often predicted exactly what I needed with minimal prompting.
+
Context-Aware Completions: Once I established patterns in my codebase, Copilot quickly recognized and extended them. For example, after implementing one API endpoint, Copilot accurately suggested the structure for subsequent endpoints following the same pattern.
+
Documentation Generation: When I started typing comments or docstrings, Copilot often completed them accurately, saving significant time on documentation.
+
+
+
Copilot's Limitations
+
+
However, I also encountered several limitations:
+
+
+
Limited Project-Wide Context: Copilot struggles to understand relationships between files unless they're explicitly imported or referenced in the current file.
+
Repetitive Patterns: When generating similar code blocks multiple times, Copilot occasionally falls into repetitive patterns that require manual correction.
+
"Happy Path" Focus: Copilot tends to generate optimistic code that handles the expected flow but often misses edge cases and error handling unless explicitly prompted.
+
Dependency on Clear Syntax: In files with complex or inconsistent syntax, Copilot's suggestions become less reliable.
+
+
+
Real-World Example: API Endpoint Implementation
+
+
Here's a concrete example from the API service development project. I needed to implement several CRUD endpoints for a user resource. After implementing the first endpoint manually:
Copilot immediately suggested the entire implementation, including validation, error handling, and response formatting in the same style as my previous endpoint. This accuracy increased as I implemented more endpoints, with Copilot learning my error handling patterns and validation approach.
+
+
Cursor: Strengths and Limitations
+
+
Where Cursor Excels
+
+
Cursor brought different strengths to the table:
+
+
+
Multi-File Context: Cursor demonstrated a superior understanding of the entire project structure, often making suggestions that referenced code in other files without explicit imports.
+
Chat-Based Interactions: The ability to have natural language conversations about code problems led to more thoughtful solutions for complex challenges.
+
Complex Refactoring: When asked to refactor code across multiple files, Cursor provided comprehensive plans and implementations that considered ripple effects throughout the codebase.
+
Debug Assistance: Cursor excelled at analyzing error messages and suggesting fixes, often correctly identifying root causes that weren't apparent to me initially.
+
Test Generation: When asked to generate tests, Cursor created more comprehensive test suites with better edge case coverage than Copilot.
+
+
+
Cursor's Limitations
+
+
Cursor wasn't without its challenges:
+
+
+
Occasionally Overthinking: For simple tasks, Cursor sometimes suggested overly complex solutions when a straightforward approach would suffice.
+
IDE Familiarity: As someone deeply accustomed to VS Code, adapting to Cursor's slightly different interface added a learning curve.
+
Response Latency: Especially for complex multi-file operations, Cursor occasionally had noticeable processing delays.
+
+
+
Real-World Example: Data Processing Challenge
+
+
During the data processing project, I encountered a complex challenge working with irregularly structured JSON data. I needed to normalize it into a consistent format for database storage.
+
+
After struggling with a particularly tricky transformation, I described the problem to Cursor in natural language:
+
+
+ "I need to transform this nested JSON with inconsistent field names. Sometimes the customer data is under 'user_info' and sometimes under 'customer_data'. Some records have nested addresses while others have flattened address fields. I need everything in a consistent format."
+
+
+
Cursor not only generated a solution that handled all the edge cases I described, but it also:
+
+
+
Created a schema validation function to verify the transformation was successful
+
Added comprehensive error handling for malformed records
+
Included detailed comments explaining the transformation logic
+
Generated unit tests for the transformation function
+
+
+
This level of comprehensive understanding and solution development would have been difficult to achieve with Copilot's inline suggestion model.
+
+
Learning Curves and Adaptation
+
+
An important aspect of working with AI coding assistants is the adaptation period and how the tools learn from your coding style.
+
+
Copilot's Learning Curve
+
+
I found that Copilot's effectiveness improved notably over time as it learned my coding patterns. By day 3 of using it on a particular project, its suggestions became significantly more accurate. The key adaptations I made to work effectively with Copilot were:
+
+
+
Writing clear, descriptive function and variable names
+
Using comments to provide context for complex operations
+
Establishing consistent patterns within a file before relying on auto-completion
+
Using Tab completion judiciously rather than accepting every suggestion
+
+
+
Cursor's Learning Curve
+
+
Working with Cursor effectively required a different approach:
+
+
+
Learning to formulate clear chat queries about code problems
+
Understanding when to use chat versus inline completions
+
Building context through project exploration
+
Providing feedback on suggested solutions to improve future recommendations
+
+
+
I found that the adaptation period for Cursor was slightly longer but ultimately led to more transformative productivity gains, especially for complex tasks.
+
+
Choosing the Right Tool for Different Tasks
+
+
After a month of intensive usage, I've developed a framework for choosing between these tools based on the task at hand:
+
+
When to Choose Copilot
+
+
+
Rapid Prototyping: When you need to quickly implement standard patterns and boilerplate
+
Well-Defined Tasks: For implementations where the structure is clear and follows established patterns
+
Documentation Writing: When generating comments, docstrings, and technical documentation
+
Repetitive Code Generation: For creating multiple similar components or functions
+
Familiar Codebases: When working in a project where patterns are already established
+
+
+
When to Choose Cursor
+
+
+
Complex Problem Solving: When you need to reason through intricate coding challenges
+
Multi-File Refactoring: For changes that affect multiple parts of a codebase
+
Debugging Difficult Issues: When tracking down elusive bugs or understanding error messages
+
Learning New Libraries or Frameworks: When you need contextual explanations along with code
+
Test Development: For creating comprehensive test suites with good coverage
+
Unfamiliar Codebases: When working in complex projects you didn't create
+
+
+
The Future of AI Pair Programming
+
+
My month-long experiment has convinced me that AI pair programming tools have fundamentally changed how developers work. I found myself thinking differently about coding problems, often framing them in ways that would be more easily communicated to an AI assistant.
+
+
Looking ahead, I expect to see several developments in this space:
+
+
+
Convergence of Features: The distinction between tools like Copilot and Cursor will likely blur as they adopt each other's strongest features.
+
Deeper Project Understanding: Future iterations will likely have even better comprehension of entire codebases, including nuanced relationships between components.
+
Personalization: AI assistants will become increasingly tailored to individual developer styles and preferences.
+
Integration with Development Workflows: We'll see tighter integration with other parts of the development process like code review, deployment, and monitoring.
+
+
+
Conclusion: Finding Your AI Pair Programming Style
+
+
+
The most valuable insight from this experiment wasn't determining which tool is "better," but understanding how to adapt my development approach to leverage both tools effectively. The future of software engineering isn't just about having AI assistants write code for us; it's about finding new workflows that combine human creativity and expertise with AI capabilities.
+
+
In the coming months, I plan to explore how these tools integrate with other aspects of the development lifecycle, from planning and architecture to testing and deployment. The transition from manual coding to AI-assisted development is just beginning, and I'm excited to continue documenting this journey.
+
+
+ AI
+ Copilot
+ Cursor
+ AI-Assisted Development
+ Pair Programming
+
+
\ No newline at end of file
diff --git a/_posts/2025-04-16-prompt-engineering.md b/_posts/2025-04-16-prompt-engineering.md
new file mode 100644
index 0000000..4f641f7
--- /dev/null
+++ b/_posts/2025-04-16-prompt-engineering.md
@@ -0,0 +1,431 @@
+---
+layout: post
+title: "The Developer's Guide to Prompt Engineering for Coding Assistants"
+date: 2025-04-16
+read_time: 8 min
+categories: [ai-assisted-development]
+permalink: /blog/prompt-engineering-coding-assistants
+excerpt: "A guide to crafting effective prompts for AI coding assistants, with practical techniques to dramatically improve code generation results"
+image: /branding/posts/prompt-engineering.png
+---
+
+
+
+ April 16, 2025
+ AI-Assisted Development
+
+
The Developer's Guide to Prompt Engineering for Coding Assistants
+
Practical techniques to get dramatically better results from AI coding tools
+
+
+
+ Katie Stoltz
+ Software Developer
+
+
+
+
+
+
+
+
+
+
+
In my previous posts, I've explored the evolution of software engineering tools and compared different AI coding assistants. One theme has consistently emerged through my work with these tools: the developer's ability to effectively communicate with AI assistants has become a crucial skill in modern software development.
+
+
The difference between a mediocre AI suggestion and an exceptional one often comes down to how you frame your request. This is where prompt engineering comes into play. The art and science of crafting inputs that yield optimal outputs from AI coding assistants.
+
+
This guide distills what I've learned about prompt engineering specifically for coding assistants like GitHub Copilot, Cursor, Claude, and ChatGPT. These techniques have dramatically improved my results when working with these tools.
+
+
The Fundamentals of Prompting Coding Assistants
+
+
Before diving into specific techniques, let's establish some core principles that apply across all AI coding assistants:
+
+
1. Be Clear and Specific
+
+
AI coding assistants aren't mind readers. The more specific and clear your prompt, the better the response you'll receive. Consider these two prompts:
+
+
+
+
❌ Vague Prompt
+
Write a function to sort an array of user data.
+
+
+
+
✅ Specific Prompt
+
Write a JavaScript function that sorts an array of user objects by their creation date in descending order. Each user object has properties: id, name, email, and createdAt (an ISO date string).
+
+
+
+
The second prompt provides crucial context about the language, data structure, and sorting criteria, dramatically increasing the likelihood of getting usable code on the first try.
+
+
2. Provide Context
+
+
AI assistants perform better when they understand the broader context of your task. This includes:
+
+
+
The project's purpose
+
Relevant technologies and libraries
+
Coding standards or patterns you're following
+
Any constraints or requirements
+
+
+
For example:
+
+
+
+
I'm building a React site using TypeScript and Tailwind CSS. We're following a functional component pattern with hooks. I need a product card component that displays an image, title, price, and "Add to Cart" button. The component should handle loading states and display a skeleton while images are loading.
+
+
+
+
This context allows the AI to align its response with your project's architecture and requirements.
+
+
3. Use a Consistent Mental Model
+
+
Think of AI coding assistants as junior developers who are extremely eager to help but need clear guidance. They:
+
+
+
Have extensive knowledge but limited understanding of your specific goals
+
Can follow patterns but need examples
+
Require feedback to improve
+
Benefit from iterative refinement
+
+
+
This mental model will help you communicate more effectively with AI tools.
+
+
Advanced Prompting Techniques for Developers
+
+
Now that we've covered the basics, let's explore more sophisticated techniques that can significantly enhance your results.
+
+
1. The "Follow the Pattern" Technique
+
AI coding assistants excel at recognizing and extending patterns. When you need code that follows an existing pattern in your codebase, show an example first
+
+
+
Following the same pattern as fetchUsers above and error handling approach, implement a function to fetch a list of products by category.
+
+
+
+
+
This approach yields code that's consistent with your existing codebase, reducing the need for adjustments.
+
+
2. The Persona Technique
+
Assigning a specific persona to the AI can significantly improve the quality and style of the code it generates:
+
+
+
+
Act as a senior security engineer who's reviewing a Node.js authentication system. Examine the following JWT validation middleware and identify any security vulnerabilities or best practices that aren't being followed:.
By defining a specific perspective, you'll get more specialized and focused feedback than a generic code review request.
+
+
3. The Iterative Refinement Approach
+
Complex coding tasks often benefit from an iterative approach:
+
+
+
Start with a high-level request to get a basic implementation
+
Provide specific feedback on what needs improvement
+
Ask for targeted enhancements rather than complete rewrites
+
Continue refining until satisfied
+
+
+
For example:
+
+
+
+ Initial Request: "Create a React hook that manages pagination for an API that returns items in pages."
+
+
+ [AI provides a basic pagination hook]
+
+
+ Refinement: "Great start. Now add error handling and a loading state to the hook."
+
+
+ [AI enhances the code with error and loading states]
+
+
+ Further Refinement: "Add support for changing the page size and sorting parameters."
+
+
+
+
This approach produces more polished and complete code than trying to get everything right in a single prompt.
+
+
4. The Constraint Declaration Technique
+
+
Explicitly stating constraints helps prevent common issues with AI-generated code:
+
+
+
+
Write a Node.js function to process CSV files with the following constraints:
+- Must handle files potentially larger than available memory
+- Must validate the CSV structure before processing
+- Must be cancellable/abortable during long operations
+- Should not use synchronous file operations
+- Must include proper error handling for invalid files
+- Should log progress for large files
+
+
+
+
By explicitly stating what the code must and must not do, you guide the AI away from common pitfalls and toward solutions that meet your requirements.
+
+
5. The Unit Test Specification
+
+
One of my favorite techniques is to specify the behavior you want through unit tests:
Different AI coding assistants have unique strengths and characteristics. Here are specific strategies for popular tools:
+
+
GitHub Copilot
+
+
Copilot excels at inline suggestions and completing code you've started writing. To get the best results:
+
+
+
Write detailed comments before functions to guide Copilot's understanding of what you're trying to achieve
+
Start implementing the pattern you want Copilot to follow, then let it complete
+
Use descriptive variable and function names to provide additional context
+
Break complex tasks into smaller functions with clear purposes
+
+
+
Example comment to guide Copilot:
+
+
+
+
// This function validates a user registration form
+// It checks:
+// - Email is properly formatted and not already registered
+// - Password meets strength requirements (8+ chars, uppercase, lowercase, number)
+// - Username contains only alphanumeric characters and is 3-20 chars
+// Returns an object with isValid flag and errors array
+// Uses the validateEmail and checkPasswordStrength utility functions
+function validateRegistrationForm(formData) {
+
+
+
+
Cursor
+
+
Cursor combines code editing with chat capabilities, so you can:
+
+
+
Ask for explanations of complex code before modifying it
+
Request multi-file changes with detailed descriptions
+
Use the chat interface for planning before implementing
+
Ask for step-by-step refactoring guidance rather than just the end result
+
+
+
Effective Cursor chat prompt:
+
+
+
+
I need to refactor this Redux store to use Redux Toolkit. The current implementation has actions in actions.js, reducers in reducers.js, and store configuration in store.js. Please help me:
+
+1. Understand what each file is doing currently
+2. Create a plan for converting to Redux Toolkit slices
+3. Implement the changes one file at a time
+4. Explain any breaking changes I need to be aware of
+
+
+
+
ChatGPT/Claude for Coding Tasks
+
+
When using general-purpose assistants like ChatGPT or Claude for coding:
+
+
+
Set the context explicitly at the beginning of your conversation
+
Specify the role you want the AI to take (e.g., experienced developer in a particular language)
+
Provide sample code from your project to establish style and patterns
+
Ask for explanations alongside the code to ensure you understand the implementation
+
+
+
Effective initial prompt:
+
+
+
+
I'm working on a Python machine learning project using TensorFlow. I'm following a functional programming style and using type hints. I need help implementing a custom loss function for a recommendation system that incorporates both user preference and item popularity. Here's an example of how I've implemented other functions in my project:
+
+```python
+def create_embedding_model(
+ user_features: int,
+ item_features: int,
+ embedding_size: int
+) -> tf.keras.Model:
+ # Model implementation
+ ...
+```
+
+Please maintain this style in your suggestions.
+
+
+
+
Debugging AI-Generated Code
+
+
Even with optimal prompts, AI-generated code may not work perfectly on the first try. Here are strategies for effective debugging:
+
+
1. Understand Before Modifying
+
+
Before making changes to AI-generated code that isn't working, ask the AI to explain its implementation:
+
+
+
+
The function you provided is throwing an error when processing large files. Before I modify it, please explain how the current implementation works, particularly the streaming mechanism and error handling logic.
+
+
+
+
Understanding the AI's approach helps you identify where the issues might be.
+
+
2. Provide Error Information
+
+
When giving feedback about errors, be specific:
+
+
+
+
The function is failing with this error: "TypeError: Cannot read property 'map' of undefined on line 23". Here's the full stack trace and a sample of the data being processed:
+
+
+
+
Detailed error information helps the AI diagnose and fix issues more effectively.
+
+
3. Ask for Alternatives
+
+
If an approach isn't working despite fixes, ask for a completely different approach:
+
+
+
+
This approach still has performance issues with large datasets. Could you suggest an alternative implementation strategy that prioritizes memory efficiency? Perhaps using a different algorithm or data structure?
+
+
+
+
Real-World Examples: Before and After
+
+
Let's examine some real-world examples of how improved prompts transformed the quality of AI-generated code.
+
+
Example 1: Data Processing Function
+
+
+
+
Before: Basic Prompt
+
Write a function to process CSV data.
+
Result: A simple function that loads the entire CSV into memory, with no error handling or performance considerations.
+
+
+
After: Engineered Prompt
+
Create a Node.js function that processes CSV files with the following requirements:
+- Must handle CSV files potentially larger than available RAM (streaming approach)
+- Should validate the header row against an expected schema
+- Must transform specific columns (convert dates to ISO format, normalize currency values)
+- Should handle common CSV errors (mismatched columns, quoted fields)
+- Must provide progress updates for large files
+- Should output processed rows to a new CSV file
+
+Here's an example of the input format:
+```
+Date,Customer,Amount,Currency,Description
+01/15/2025,Acme Inc.,1234.56,USD,"Annual subscription"
+```
+
+And the expected output format:
+```
+date,customerId,amountUsd,description
+2025-01-15T00:00:00Z,ACME001,1234.56,"Annual subscription"
+```
+
+The solution should use the 'csv-parser' library for reading and 'csv-writer' for writing.
+
Result: A robust streaming solution with proper error handling, progress reporting, and memory efficiency.
+
+
+
+
+
Common Pitfalls to Avoid
+
+
Even with good prompt engineering techniques, there are common mistakes that can lead to suboptimal results:
+
+
1. Being Too Vague or Too Verbose
+
+
Finding the right balance is key. Too vague, and the AI lacks necessary context; too verbose, and you might overwhelm it with irrelevant details.
+
+
2. Forgetting About Edge Cases
+
+
AI assistants tend to focus on the happy path unless explicitly directed to handle edge cases. Always specify how your code should handle errors, edge cases, and unexpected inputs.
+
+
3. Not Providing Examples
+
+
Examples of input/output or code style dramatically improve results. Without examples, the AI has to make many more assumptions.
+
+
4. Treating AI-Generated Code as Infallible
+
+
Always review and test AI-generated code. These tools are assistants, not replacements for developer judgment and testing.
+
+
5. Not Iterating on Prompts
+
+
If you don't get the results you want, refine your prompt rather than starting over completely. Each interaction teaches you how to better communicate with the AI.
+
+
Conclusion: The Future of Developer-AI Collaboration
+
+
Prompt engineering for coding assistants is still an evolving discipline. As AI tools continue to advance, the ways we interact with them will also evolve. However, the fundamental principles of clear communication, providing context, and iterative refinement will remain valuable.
+
+
The most effective developers will be those who view AI assistants as collaborative partners rather than simply code generators. By understanding how to effectively communicate your requirements, constraints, and preferences, you can transform these tools from interesting curiosities into powerful productivity multipliers.
+
+
In my next post, I'll explore how these AI coding assistants can be integrated into larger development workflows, including code review, documentation, and team collaboration. Until then, I encourage you to experiment with these prompting techniques and start building your own library of effective prompts for your common development tasks.
+
+
+ AI
+ Prompt Engineering
+ Coding
+ AI-Assisted Development
+ Developer Tools
+
+
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+---
+layout: default
+title: About
+stylesheet: about
+---
+
+
+
+
About Me
+
+
+
+
Hi, I'm Katie Stoltz
+
I'm a software developer passionate about the evolving landscape where software engineering meets AI innovation. With experience in both traditional development and emerging AI technologies, I created Stoltz Stack to explore how artificial intelligence is transforming the way we build software.
+
My journey in tech has given me a front-row seat to the rapid evolution from manual coding to AI-assisted development. Through this blog, I share insights, tutorials, and perspectives on AI tools, machine learning systems, and the changing practices of software engineering.
+
+
+
+
+
+
My Journey
+
After graduating college, I began my career at a cybersecurity startup where I quickly rose to the position of CTO. This experience gave me invaluable insights into security-minded development practices and the critical intersection of software reliability and protection against threats.
+
As AI technologies began transforming the development landscape, I made the decision to pivot my career toward AI development. This transition allowed me to combine my security background with cutting-edge AI tools and methodologies, giving me a unique perspective on how these technologies can work together to create more robust software solutions.
+
Today, I'm focused on exploring how AI is reshaping the software engineering discipline while maintaining the security principles that have guided my career from the beginning. Through Stoltz Stack, I aim to share these experiences and insights with the broader developer community.
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+layout: default
+title: Blog
+stylesheet: blog
+---
+
+
+
+
Blog
+
Explore our latest articles on AI-assisted development, AI tools and applications, software engineering practices, and AI research.
+ 
+ 
+ 
+
+
+
+