EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Aug 17·5 min read
Lobsters·Aug 17·5 min readAI Software Development – What Does The Data Say?
TL;DR
Recent studies on large language models (LLMs) reveal their growing role in software development, highlighting both their potential and limitations. Insights from various sources, including peer-reviewed research and personal experiments, provide a clearer understanding of LLMs' effectiveness in coding tasks.
✦ Why It Matters
Evaluate your team's use of LLMs in coding tasks to optimize their effectiveness today.
Key Takeaways
- Limit context size to improve LLM output accuracy.
- Avoid including excessive documentation in prompts.
- Use examples instead of instructions for better results.
- Monitor team morale and cognitive load when using LLMs.
How It Works
LLMs generate outputs based on patterns learned during training, but they struggle with long-range dependencies and context dilution, leading to inaccuracies.
⚠ The Catch
LLMs cannot reliably distinguish between recent and outdated information, often prioritizing learned biases over new context provided.