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Banking Technology
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- Article · AI Agents
Beyond Prompt Engineering: When to Pivot to Architecture in Enterprise AI
Prompt engineering is the "first line of defense" in LLM applications. It is cost-effective and fast. However, in enterprise scenarios—especially in regulated industries like banking—there is a clear tipping point where tweaking prompts yields diminishing returns, and architectural changes become mandatory.
- Article · AI Agents
Building an Enterprise-Grade Bank Policy Assistant with LangGraph: Multi-Agent RAG with Memory and State
Before diving into the architecture, let’s address the core question regarding Chain-of-Thought (CoT) and Few-Shot Prompting: Factualness: Both techniques significantly improve factual accuracy in complex domains like banking. CoT forces the model to "show its work," reducing hallucinations by breaking down logical steps (e.g., "First, check the customer's tenure; second, verify the loan type..."). Few-Shot provides concrete examples of correct reasoning patterns, anchoring the model to specific compliance standards.
- Article · AI
Why AI Is Becoming the Intelligence Layer of Financial Services in 2026
Discover how AI is becoming the intelligence layer of financial services in 2026. Learn how financial institutions use AI for fraud detection, predictive analytics, compliance automation, AI agents, and responsible governance to improve decision-making, customer experiences, and operational efficiency.
