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Improve RAG retrieval accuracy
Ensure LLM responses are grounded by business and domain knowledge with document metadata, optimized chunking, and fine-tuned embedding models.
Optimize RAG to ensure LLM responses are grounded by SME knowledge
Meet production accuracy needs
RAG pipelines often fail to meet production accuracy needs out of the box, but optimization can yield significant improvements in retrieval accuracy—and thus LLM response accuracy.
Reduce inference token costs
With more precise chunking and accurate retrieval, only the most relevant information is added as context for the LLM, reducing the number of input tokens—and thus inference costs.
Improve LLM response quality and latency
By utilizing context windows much more efficiently, optimized RAG pipelines not only result in higher-quality responses from the underlying LLM, but can reduce the response time as well.
Overcome training data shortages
Snorkel Flow can generate synthetic prompts from unstructured data by prompting foundation models such as OpenAI GPT and Meta Llama to augment existing training data.
Why do standard RAG pipelines fail to generate accurate responses?
While out-of-the-box RAG pipelines are an easy way for enterprises to get started with LLMs, they often fail to meet production accuracy requirements. The problem is they simply don’t know enough about the domain to ensure the right information is being fetched. However, once adapted to enterprise documents and use cases, they can consistently provide LLMs with the most relevant and helpful context—nothing more, nothing less.
Add document metadata to improve search
Snorkel Flow’s information extraction capabilities can be used to label document chunks with helpful metadata before adding them to a vector store. This allows the RAG pipeline to retrieve relevant chunks by combining both similarity search and filtering, improving search accuracy as well as latency.
Optimize chunking to remove noise
RAG frameworks such as LlamaIndex and LangChain support basic chunking. However, using a fixed chunk size creates chunks with partial and/or unrelated information. Snorkel Flow solves this problem by chunking documents based on their structure and content, removing noise and ensuring relevant information remains intact.
Fine-tune models to improve accuracy
The problem with out-of-the-box embedding models is they have a hard time separating relevant and irrelevant information within specific domains. With Snorkel Flow, AI teams can easily curate high-quality training data and fine-tune embedding models to improve retrieval accuracy – and the accuracy of LLM generated responses as a result.
Dive deeper into RAG optimization with these resources
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