GenAI Productionize 2.0: The premier conference for GenAI application development
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Fine-tuning and RAG help get the most out of LLMs (using synthetic data or otherwise), but even then you may be legally liable for hallucinations. Check out our roundup of the top generative AI and LLM articles for March 2024!
In a first, an LLM hallucination has caused Air Canada to lose in court. Companies are liable for what their chat apps generate, making LLM evaluation and observability more important than ever: https://mashable.com/article/air-canada-forced-to-refund-after-chatbot-misinformation
From mis-ranked documents to extraction failures, researchers have identified what causes issues in RAG systems and lessons learned: https://www.latent.space/p/neurips-2023-papers
Climate change affects us all. Here are a few climate tech startups using GenAI to help stop the climate crisis: https://aws.amazon.com/blogs/startups/how-climate-tech-startups-use-generative-ai-to-address-the-climate-crisis
There's a whole buffet of LLMs available for use, but to get real value out of them AI teams need to fine-tune models on their own data. Here’s a great guide on how to get it done: https://www.philschmid.de/fine-tune-llms-in-2024-with-trl
There's much hype (and concern) about AGI and superintelligence. But let's not disregard our current drive towards "capable" AI. Learn why this un-hyped phase deserves more respect and attention: https://www.turingpost.com/p/capableai
Synthetic data is increasingly viable for pretraining, instruction-tuning, and preference-tuning. Do you know the two main approaches – distillation and self-improvement – for this cheaper, faster alternative to human annotation? https://eugeneyan.com/writing/synthetic
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