At the core of Snorkel’s approach and platform is the concept of programmatic data development, which we have repeatedly proven to accelerate training of predictive models by leveraging SME knowledge to produce labels efficiently. At Snorkel Research we have been extending this approach to Generative AI, where the output is not just labels but long form and complex. Our strategy has been to develop methods and tools to keep SMEs in the loop of training and evaluation with scalable processes.

In this presentation, Chris Glaze, Principal Research Scientist at Snorkel AI will give a brief history of how Snorkel evolved from data labeling to data development for Generative AI, and share stories on how Snorkel’s applied this approach to Fortune 500 use cases. Angela Fox, Staff Product Designer at Snorkel AI, will demonstrate how users can leverage Snorkel Flow’s unique programmatic labeling features to solve both predictive and generative business challenges.

PDF documents represent a vast, untapped source of enterprise data, posing unique challenges due to their diverse formats and structures. From messy, massive files to varying types of content such as text, multi-columns, and tables, extracting valuable insights from these documents requires innovative solutions.

In this session, we will explore how to unlock the full value of your PDF documents, from simple classification to the most complex, high-value extraction use cases, all using a single Snorkel Flow platform. By leveraging OCR and parsing, using a collaborative platform for domain experts and data scientists, and utilizing leading LLMs or models of your choice, you can easily overcome format variations, ensure accurate information extraction, and efficiently build custom PDF use cases.

Join us to discover how Snorkel AI’s integrated platform can help you navigate the complexities of PDF document processing and fully leverage the potential of your enterprise data.

LLM evaluation is critical for generative AI in the enterprise, but measuring how well an LLM answers questions or performs tasks is difficult. Thus, LLM evaluations must go beyond standard measures of “correctness” to include a more nuanced and granular view of quality.

In practice, enterprise LLM evaluations (e.g., OSS benchmarks) often come up short because they’re slow, expensive, subjective, and incomplete. They leave AI initiatives blocked because there is no clear path to production quality.

In this session, Venkatesh Rao, Staff Product Manager at Snorkel AI, and Rebekah Westerlind, Software Engineer at Snorkel AI, will discuss the importance of LLM evaluation, highlight common challenges and approaches, and explain the core concepts behind Snorkel AI's approach to data-centric LLM evaluation.

Join us to learn more about:

• Understanding the nuances of LLM evaluation
• Evaluating LLM response performance at scale
• Identifying where additional LLM fine-tuning is needed

In the realm of LLM-powered AI applications, Retrieval-Augmented Generation (RAG) is a pivotal component for enterprise use cases. However, to ensure responses are consistently accurate, helpful, and compliant, RAG pipelines must undergo meticulous optimization.
 
Critical to this process is the incorporation of only the most relevant information as context. This can be achieved through techniques such as semantic document chunking, fine-tuned embeddings, reranking models, and efficient context-window utilization.
 
In this presentation, we will:

1. Introduce fundamental RAG concepts and outline a standard pipeline.
2. Detail optimization strategies for each stage of a sophisticated RAG pipeline to ensure the LLM receives proper context.
3. Demonstrate how to leverage Snorkel Flow to optimize RAG pipelines.

By attending, you will gain insights on how to:

• Enhance LLM responses by minimizing retrieval errors.
• Fine-tune various stages of the RAG pipeline.
• Expedite the deployment of production-grade RAG applications.

Join us to elevate your RAG systems and drive superior AI outcomes for your enterprise.

LLMs often require fine-tuning and alignment on domain-specific knowledge before they can accurately, and reliably, perform specialized tasks within the enterprise.

The key to transforming foundation models such as Meta's Llama 3 into specialized LLMs is high-quality training data which can be applied via fine-tuning and alignment.

In this session, we'll provide an overview of methods such as SFT and DPO, show how to curate high-quality instruction and preference data 10-100x faster (and at scale) and demonstrate how to fine-tune, align and evaluate an LLM.

Join us, and learn more about:

• Curating high-quality training data 10-100x faster
• Emerging LLM fine-tuning and alignment methods 
• Evaluating LLM accuracy for production deployment