RAG (Retrieval-Augmented Generation) allows LLMs to access real-time or private data without retraining. It minimizes hallucinations by forcing the model to cite specific context from your vector database, making it essential for enterprise-grade AI which requires 100% accuracy and groundedness.

Agentic workflows involve using AI to plan, reason, and execute tasks autonomously. Instead of a single prompt-response, agents use tools (APIs, search, code execution) to solve complex problems over multiple iterations, providing a much higher success rate than zero-shot prompting.

Key strategies include aggressive semantic caching with tools like Redis or Upstash, using Small Language Models (SLMs) for classification tasks, implementing token streaming for better perceived UX, and utilizing edge-based inference to reduce round-trip times.

We recommend a multi-layered approach: strict grounding via RAG, using NLI (Natural Language Inference) for verification, implementing 'Chain of Thought' prompting, and using secondary 'Evaluator' models to audit the primary model's outputs before they reach the user.