The 4 Big Changes in LLMs
AI Summary
Summary of Key Takeaways from San Francisco Visit
- Models Getting Smarter
- New models like Anthropic Sonnet 3.5 are being released.
- Sam Altman’s perspective: startups should assume models will improve and design accordingly.
- Current models may be expensive, but smarter models are emerging without increased costs.
- Synthetic Data
- Synthetic data is being used for training, leading to stronger models.
- It allows for high-quality, tailored data for instruction fine-tuning and alignment.
- Multimodality
- Multimodal models can ground knowledge better, enhancing their capabilities.
- Tokens Getting Faster
- Innovations like Grok are speeding up token generation.
- Faster models enable new strategies like polling, reflection, and verification.
- Tokens Getting Cheaper
- The cost of tokens is predicted to drop significantly by year’s end.
- Cheaper, faster models like Haiku and Gemini Flash are becoming more prevalent.
- Infinite Context Windows
- Anticipated shift to models with no context window limitations.
- RAG (Retrieval-Augmented Generation) is evolving, not disappearing.
- In-context learning may replace fine-tuning, with dynamic example selection based on queries.
Design Considerations for LLM Apps
- Prepare for Changes
- Design apps with the anticipation of smarter models.
- Abstract logic in prompts for easy updates.
- Incorporate in-context learning examples.
- Data Management
- Store raw data for quick chunking and embedding variations.
- Test different RAG systems.
- Profit Implications
- Consider how cheaper and faster models affect profitability and competition.
- Early adoption can gather valuable user data before market saturation.
Conclusion
- Startups and LLM app developers should be mindful of upcoming changes in the next six months.
- Embrace the evolving landscape to stay competitive and innovative.
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