EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Aug 17·5 min read
A practical workflow for LLM-assisted development
TL;DR
A practical workflow for using large language models (LLMs) in software development reveals that while LLMs can produce useful code quickly, they require careful guidance to avoid errors. By iterating on initial outputs, developers can leverage LLMs to tackle complex projects more efficiently than traditional coding methods.
✦ Why It Matters
Start using LLMs to generate boilerplate code and explore codebases to save time on your next project.
Key Takeaways
- Define project goals and structure before using LLMs.
- Use tests as contracts to ensure functionality.
- Leverage Git for version control and experimentation.
- Maintain a clean architecture to minimize dependencies.
How It Works
The author likens the LLM coding process to a genetic algorithm, where initial code outputs are refined through iterative feedback. By providing clear requirements and structured tasks, developers can guide the LLM to produce more relevant and functional code.