Here is a nice case study on how Gen AI, using RAG techniques, enhanced the operations of an enterprise. Many enterprise platforms struggle with underutilized data or inertia due to factors like large databases, traceability issues, entity mapping challenges, and a lack of data experts. With the rise of Gen AI, we now have better methods that provide surprisingly fast, effective solutions for generating the right business intelligence and improved decision-making.
Here are my additional thoughts:
Solutions are always tailored to specific problems. There’s no one-size-fits-all approach. The techniques vary depending on needs like the level of customization, available data sources, and system complexity. The strategy should be based on these factors. Here are a few alternative approaches we can consider, if RAG is optional:
- Embedding-Based Search with Pretrained Models: This is a relatively easy approach to implement, but it doesn’t offer the same capabilities as RAG. It works well when simple retrieval is enough and there’s no need for complex reasoning.
- Knowledge Graphs with AI Integration: Best for situations where structured reasoning and relationships are key. It requires manual effort and can be tricky to integrate, but it offers powerful semantic search capabilities and supports reasoning tasks.
- Fine-Tuned Language Models: This is ideal for stable, well-defined datasets where real-time data isn’t crucial. Since the data is straightforward, generating responses is easier. It performs well when the data is comprehensive but may struggle with queries outside the trained data.
- Hybrid Models: A mix of retrieval and in-context learning. While it’s a bit more complex to implement, it delivers high accuracy and flexibility because it combines different techniques. Use this when you need high accuracy and rich content.
- Multi-Modal Models: These models handle different types of data (eg., images, text) and provide combined insights. For example, they can retrieve images from documents and analyze them. However, they require solid infrastructure, which can get expensive.
- Rule-Based Systems: These expert systems rely on predefined rules to generate responses. They’re great for regulated industries like finance & legal, as they offer transparency and auditability. However, they’re typically not scalable and may not handle unstructured data effectively.
- End-to-End Neural Networks (for Q&A): These models are trained specifically for question-answering tasks. They perform well for defined tasks like Q&A and give concise answers without the need for complex pipelines. But they require large, annotated datasets and may underperform if there isn’t enough related data.
Since this field is still evolving, it’s important to stay on the lookout for new or improved techniques based on the specific requirements
Enterprises are turning to Generative AI to build the next generation of their core platforms.
Learn how a global enterprise enhanced their smart data platform with Generative AI capabilities as a proof of concept. Read the success story: https://lnkd.in/dRUZgEEm
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