The Foundation: LLM Workflows

An LLM workflow is the foundational building block for all other AI applications. It is not a single tool but a series of steps that use an LLM to solve a problem.

• What it automates: Content creation, customer support, data analysis, and fraud detection.
• Example: A simple content workflow can take a prompt, generate an outline, and draft a section of an article.
• Business Value: The initial use cases that help businesses begin their AI journey.

The Engine of Accuracy: RAG

Retrieval-Augmented Generation (RAG) is a critical solution that addresses a major pain point for business leaders: AI "hallucinations" and outdated information.

• How it works: RAG provides a constantly updated and authoritative knowledge base for an LLM to pull from.
• Business Value: This is the key to building trust. It ensures AI responses are:
  
  • Accurate and verifiable.
  • Traceable to source documents.
  • Transparent, directly addressing concerns about data accuracy and bias.

The Power of Autonomy: AI Agents

AI agents are the next evolution of LLM workflows. Unlike a simple workflow that follows fixed steps, an AI agent can plan, reason, and act on its own to achieve a goal.

• What it automates: Complex, multi-step workflows in HR, IT support, customer service, and finance.
• Key Differentiator: It makes decisions and takes action independently.
• Business Value: This ability to handle multi-step workflows with context awareness and traceability is what makes AI agents so powerful.

The AI Toolkit at a Glance

This table connects each technology to a tangible business problem, showing AI as a practical problem-solving toolkit.

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