Artificial intelligence (AI) has shifted from a bold experiment to a strategic necessity. Recent studies show that 88% of C-level decision-makers want to accelerate AI adoption in 2025. Yet, just a fraction of AI initiatives delivers the expected results.

TL;DR Many companies rush into generative AI without realizing just how steep—and unpredictable—the associated expenses can be. Meanwhile, the cost of implementing generative AI in business can range from $0.0005 per 1,000 text units (tokens or characters) if you use a popular commercially available Gen AI tool to over $190,000 for a fully custom solution based on a fine-tuned open-source model.

Retrieval-augmented generation (RAG) is an AI architecture that enhances LLM accuracy by dynamically retrieving relevant, up-to-date information from external knowledge sources RAG significantly reduces hallucinations and improves response accuracy in critical domains like healthcare (96% diagnostic accuracy) and legal (38-115% productivity gains)