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Supercharging the Amazon Q Developer Dev Agent

Supercharging the Amazon Q Developer Dev Agent

Explore an innovative technique for Amazon Q Developer that combines chat and dev agent to produce better code implementations.

Published Jan 24, 2025
Last Modified Jan 27, 2025

Introduction

The most powerful solutions often emerge from unexpected discoveries. Recently, while working on a feature for promptz.dev, I stumbled upon a technique that dramatically improved my interaction with Amazon Q Developer. This discovery not only enhanced the quality of the generated code but most importantly made the entire development process feel more natural and effective.
I initially approached Amazon Q's dev agent with straightforward prompts, expecting it to understand my requirements immediately. The results were mixed – sometimes spot-on, other times missing crucial context that seemed obvious to me but wasn't explicitly stated in my prompts.
The breakthrough came trying a different approach. Instead of jumping straight to the dev agent, I found myself in a natural conversation with Q Developer about the feature requirements. As the discussion evolved, something fascinating happened.

The Context Challenge

As developers embrace AI coding assistants, we often overlook a fundamental truth about these tools: they're only as good as the context we provide them. During my work with Amazon Q Developer and Generative AI in general, I've come to appreciate that context isn't just helpful—it's essential for generating reliable, and accurate outputs.
The relationship between input and output in AI coding assistants follows a simple yet powerful formula as described by Ricardo Suieras in his awesome series of Amazon Q Developer Tips:
Prompt + Context = Output
While this equation might seem obvious, its implications run deep. What fascinates me most is how context reaches the AI assistant through both explicit and implicit channels.
Explicit Context is what we consciously provide:
  • Project requirements in our prompts
  • Code snippets we share
  • Architecture decisions we explain
  • Business rules we define
Implicit Context comes from how the tools themselves work. The Amazon Q dev agent, for example, is equipped with “tools” to explore files, search files, modify files, add, or remove files, or undo previous changes in an internal text-based IDE. The agent selects the tool and uses the tool on the environment - as of today limited to the source code repository.
Think of context as a two-way street. What you tell the AI assistant directly is just as important as what it can discover through its built-in capabilities. Understanding this dual nature of context has transformed how I approach AI-assisted development. I've learned to be deliberate about explicit context while leveraging the tool's implicit context-gathering capabilities.
A well-crafted prompt alone isn't enough to generate high-quality code. Without proper context, even the most carefully written prompt can lead to implementations that miss critical requirements or fail to align with existing system architecture.
Expecting the AI to understand our codebase's conventions without explicitly sharing them, and providing fragments of what we need while keeping crucial details in our heads are typical anti-patterns I encounter that I recognize as context-related issues.

A New Approach: The Chat-to-Agent Technique

I've discovered that the most effective way to leverage its capabilities is to mirror how we naturally collaborate with our human peers. Instead of jumping straight into implementation with the dev agent, I start with a conversation.

Start with Conversation

The key to successful AI-assisted development lies in how we initiate the dialogue. When explaining a new feature to a peer engineer, we don't start with implementation details – we begin with the problem we're trying to solve. Here's how I started the conversation about a feature for promptz.dev that should allow users to mark submitted prompts as their favorites:
As more and more prompts are being submitted it gets harder for users to discover relevant prompts for their use-cases. What changes needs to be implemented in this @workspace to allow users mark prompts as favorites?"
This open-ended question led to a natural exploration of UX considerations, data model implications, performance requirements, and implementation constraints.
💡 Pro Tip: Use the @workspace context modifier in your initial question. This automatically includes relevant chunks of your workspace code as context.

Clarifying Requirements Through Dialogue

The beauty of this conversational approach is how naturally it surfaces important considerations. The conversation evolved organically. The initial proposal of Q Developer had some pitfalls that I wanted to clarify, so I asked:
If multiple users would favorite the same prompt at the same time, wouldn't this result in data inconsistencies?
This led to a deeper discussion about, race conditions in concurrent operations, the need for atomic updates, data consistency guarantees, and alternative implementation approaches. This mirrors how technical discussions flow in real engineering teams, where requirements and constraints emerge through dialogue rather than being fully formed from the start.

Crafting the Perfect Dev Agent Prompt

Once the discussion clarified all aspects of the feature, Q Developer demonstrated another powerful capability – generating an optimal prompt for its dev agent based on our conversation. The resulting prompt was remarkably precise, incorporating all the nuances and edge cases we'd discussed.
💡 Pro Tip: Don't rush to implementation. Let the conversation continue until you see the requirements crystallize into a clear implementation path.
This natural progression from conversation to implementation helps maintain alignment between business requirements and technical solutions throughout the development process.

A Real-World Implementation

Let me walk you through how this chat-first technique transformed a seemingly simple feature request into a robust implementation. The initial feature request for promptz.dev was straightforward: allow users to mark prompts as favorites.

Initial Approach vs. Chat-First Method

My initial attempt was typical of how I approached the dev agent until now. Jump straight to the dev agent, provide a basic feature description, and start implementing right away. Here's what I initially sent to the dev agent:
The result? Functional code, but missing crucial elements. The Dev Agent did not understand that PROMPTZ is built on top of AWS Amplify Gen2 and suggested a brand new GraphQL schema file to implement the data model. The implementation also misses crucial elements like atomic operations for concurrent updates. As the data model implementation was not accurate, data fetching implementations built on top were factually incorrect.
Using the chat-first technique, the conversation naturally surfaced critical functional and non-functional requirements that were discovered. This is the prompt that Amazon Q Developer created for me:
The implementation plan that emerged was comprehensive and production-ready. Here's what made the difference in my observation:
  • Accurate Data Model Evolution: The discussion and associated context led to a proper data model based on AWS Amplify Gen2 including the correct authorization mechanism.
  • Better modularization: The dev agent encapsulates logic in new react components like a `FavoriteToggle.tsx` component and reflects the current logic of implementing data fetching using react hooks.
The most striking difference between the two approaches wasn't just in the code quality - it was in the completeness of the solution. The chat-first approach surfaced edge cases early in the development process, led to better design decisions, and produced better maintainable code.
I guess I would have received similar results by providing the dev agent with more feedback after the first implementation plan. However, with the chat-to-agent approach, I was able to shift important reasoning steps from the agent to me using the chat and explicitly provided context - making me the human in the lead. That drastically reduced my time to first commit to getting this feature in production.
The key elements that made the prompt effective were clear specifications and precision. You can do a quick litmus test and ask yourself: “If I were an engineer, what would help me to implement this feature? The version that Amazon Q Developer created, or my initial naive approach below?”

Looking Forward

During my recent conversation with Ricardo Sueiras from AWS, I learned that he follows a similar pattern. His workflow involves capturing chat outputs into local workspace files, which he then references in his dev agent prompts or uses as implicit context using the `@context` modifier. This manual approach works because the dev agent is already equipped with file-related tools that can explore and understand the workspace context.
This leads me to an interesting question: What if the Amazon Q Developer agents could automatically access and understand the conversation history when invoked within a chat? This capability would transform the chat-to-agent technique into a seamless, integrated workflow.
Imagine combining this contextual understanding with other powerful features of Amazon Q Developer:
  • Using chat context to generate more accurate unit tests using the test agent.
  • Leveraging discussion history for better code reviews using the review agent.
  • Enhancing documentation generation with conversational insights using the doc agent.
Looking ahead, I envision AI coding assistants that seamlessly blend conversation and implementation, much like pair programming with a highly capable colleague. The more we can bridge the gap between human communication and machine understanding, the more powerful our development workflows will become.
This isn't just about writing better code—it's about transforming how we interact with AI development tools to create better software solutions.

Conclusion

What started as a simple experiment with the favorites feature for promptz.dev revealed a powerful technique that I will try in more scenarios in the future. The power of this approach lies in its naturalness. By mirroring how we work with our peers, we might:
  • Build richer context through organic conversation
  • Surface critical requirements before implementation
  • Leverage both explicit and implicit context effectively
  • Create more maintainable and production-ready code
I encourage you to try this technique in your development workflow. Start a conversation with Amazon Q Developer about your next feature. Challenge its initial suggestions, explore edge cases, and see how the dialogue shapes the final implementation.
Share your experiences and insights with me and the wider AWS community. How does this approach work for your use cases? What patterns have you discovered?
 

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