EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 29·5 min read
Domain-Driven Agents
TL;DR
Domain-Driven Agents highlights the challenges of integrating large language models (LLMs) into legacy codebases, where their effectiveness diminishes significantly. While LLMs excel in new projects, they struggle with complex, established systems laden with technical debt.
✦ Why It Matters
Evaluate your legacy systems before deploying LLMs to ensure effective integration and avoid productivity losses.
Key Takeaways
- Use Domain-Driven Design to improve communication between business and technical teams.
- Split work into strategic decision-making and tactical implementation for efficiency.
- Utilize .workflow.json to define project domains and relationships clearly.
- Address technical debt incrementally with AI assistance to enhance legacy systems.
How It Works
The author employs a structured approach using Domain-Driven Design (DDD) to clarify the relationship between business needs and technical implementation. By defining project domains and relationships in a .workflow.json manifest, the system can better understand context and vocabulary, allowing AI agents to assist in code upgrades effectively.