Even with the advancements of Large Language Models (LLMs), there are still hurdles to overcome when it comes to applying the AI to an enterprise’s internal data.
However, there are several strategies enterprises can adopt to make LLMs work effectively to enable AI on their private data.
In this article, I will explain the concepts behind RAG, its limitations and how to overcome them, along with few real-world enterprise AI case-studies.
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The article explores the powerful synergy between Domain-Specific Language (DSL) and Small Language Models (SLM) in enabling AI for Industry 4.0.
DSL bridges the gap between semantics and syntax, enhancing code readability and reducing errors by converting complex rules into concise syntax.
When paired with SLM, this duo becomes a formidable force in catching semantic errors at the syntax level, leading to more efficient and error-free coding across various domains like healthcare, FinTech, and retail.
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