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Snorkel Enterprise AI Platform
GenAI optimization
Improve enterprise GenAI systems by combining enterprise data and subject matter expert (SME) domain knowledge with RAG optimization techniques and LLM fine-tuning or distillation methods, resulting in retrieval and generation tailored to specific domains and business use cases.
Enterprise GenAI requires specialized systems
It's easy to build prototypes of enterprise AI assistants and copilots with off-the-shelf components, but they inevitably lack the accuracy and reliability needed for production deployment. The solution is to optimize RAG pipelines and fine-tune LLMs using enterprise data and domain knowledge to create specialized GenAI systems which are adapted to specific domains and use cases.
GenAI optimization with the Snorkel Enterprise AI Platform
Add document metadata to improve retrieval
Apply programmatic information extraction to label document chunks with helpful metadata before indexing them in a vector database, enabling AI teams to improve search accuracy and latency by retrieving relevant chunks by extending similarity search with filtering.
Apply adaptive chunking to remove noise
Replace the default, token-based chunking strategies in common orchestration frameworks such as LangChain and LlamaIndex with an adaptive chunking strategy based on structure and content, removing noise and ensuring relevant information remains intact.
Fine-tune embeddings for domain accuracy
Curate training data via programmatic data labeling and synthetic data generation, and use it to fine-tune open embedding models such as those from Salesforce and Nvidia—significantly improving retrieval accuracy without having to modify source documents or code.
Curate high-quality LLM training data faster
Create a diverse set of prompt-response pairs in days by incorporating enterprise data and SME domain knowledge in the latest programmatic data development and synthetic data generation techniques, removing the need for manual efforts and weeks if not months of delays.
Gather SME input and feedback with ease
Collaborate with SMEs using a single platform to create ground truth based on domain knowledge and human feedback, and to iterate on training data by refining its quality and diversity to further improve and align LLM generation with business expectations.
Fine-tune and deploy specialized LLMs
After curating high-quality training data, use it to fine-tune and deploy specialized LLMs by taking advantage of native integration with Databricks Data Intelligence Platform (i.e., Mosaic AI and Unity Catalog) and AWS SageMaker, Google Vertex AI, and Azure Machine Learning.
Dive deeper into GenAI optimization with these resources
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