INEXTURE Solutions LLP

One of the biggest challenges in scaling AI for enterprise use is hallucinations  moments when large language models generate content that sounds confident but is factually wrong. In consumer chatbots, this may be an inconvenience. In enterprise contexts like healthcare, finance, or compliance, hallucinations can cost millions or even risk human safety. This is where Retrieval-Augmented Generation (RAG) steps in.

Retrieval-Augmented Generation (RAG) became a force in the area of accurate, context-aware generative AI use cases shortly after its release. From building chatbots to corporate search engines or custom Q&A systems, RAG core significantly enhances performance by linking LLM output to external knowledge. Hallucinations, out-of-topic retrievals, and poor context aggregation continue to be issues that tend to fool models in real-world deployment, though. This is where sophisticated RAG sets in.

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