Source URL: https://harper.blog/2025/02/16/my-llm-codegen-workflow-atm/
Source: Hacker News
Title: My LLM codegen workflow ATM
Feedly Summary: Comments
AI Summary and Description: Yes
Summary: The text presents a comprehensive guide on using LLMs (Large Language Models) for software development, detailing a structured workflow that encompasses idea generation, planning, execution, and testing. It addresses both greenfield projects and existing codebases, while also sharing practical tips, tools, and personal insights from the author’s experiences. This insight is particularly relevant for professionals in AI infrastructure, as it showcases the integration of generative AI into development processes.
Detailed Description:
– **Purpose and Approach**:
– The author shares their experience in building software products using LLMs, emphasizing the novelty of integrating AI into coding workflows.
– Acknowledges the pitfalls of LLMs while outlining their potential for productivity.
– **Workflow Stages**:
1. **Idea Honing**:
– The use of conversational LLMs, like ChatGPT, to collaboratively outline detailed specifications for a coding project.
– Focus on iterative questioning to refine ideas into developer-ready documents.
– Resulting specifications serve multiple uses, including business planning and white papers.
2. **Planning**:
– Transitioning from specifications to actionable blueprints for project execution via reasoning models.
– Emphasizes test-driven development (TDD) and maintaining small, manageable steps to avoid overwhelming complexity.
3. **Execution**:
– Choice of various tools for coding, such as GitHub workspace and specific LLMs (e.g., Claude, Aider).
– Highlights the importance of setting up boilerplate code and utilizing the LLM for actual coding tasks.
– Mentions tools like Repomix for integrating and debugging code, enhancing coding efficiency.
– **Iterative Improvement**:
– For projects not in the greenfield phase, the author proposes a flexible, task-focused method where planning is done in small, manageable portions instead of comprehensive project blueprints.
– Showcases specific tasks that can be automated using LLMs, such as code reviews and generating GitHub issues.
– **Practical Tips**:
– Shares specific prompts that can be employed with LLMs for tasks such as code reviews, GitHub issue generation, and identifying missing tests.
– Discusses the balance between efficiency gained from LLMs and the potential for missteps (“over my skies” analogy).
– **Considerations for Collaborative Coding**:
– Expresses a desire for improved collaboration tools within LLM workflows, advocating for solutions that allow multiple developers to work effectively with LLMs rather than isolated, individual efforts.
– **Reflection on AI and Productivity**:
– Highlights both the productivity gains and the environmental and ethical concerns associated with AI use, urging a balanced perspective on the involvement of LLMs in coding.
This text serves as a practical guide for professionals looking to leverage LLMs in their software development processes while providing insights into both the practical benefits and the potential challenges associated with generative AI technology.