Unlock the Power of AI Agents for Enhanced Outcomes

Agentic AI: Compound LLM Case for Marketing Plan Generation

Key Concepts:

• Agentic AI
• Compound LLMs
• Writing Agent
• Critique Agent
• LLM Workflow
• Product Description
• Marketing Plan
• Jupyter Notebook
• API Keys
• Electronic Health Record (EHR)
• FHIR (Fast Healthcare Interoperability Resources)
• OMOP (Observational Medical Outcomes Partnership)
• Prompt Engineering
• Model Selection (GPT-4o, Grok-2)
• Critique and Revision
• Policy Checking Agent
• Ethics Checking Agent
• Brand Agent

1. Introduction to Agentic AI and Compound LLMs

• The video introduces Agentic AI using a compound LLM approach, where multiple LLMs work together in a workflow to achieve a specific goal.
• The example focuses on generating a high-quality marketing plan for a new product using a writing agent and a critique agent.

2. Workflow of the Compound LLM System

• Step 1: Writing Agent: An LLM (GPT-4o) acts as a writing agent, generating an initial draft of the marketing plan based on a product description.
• Step 2: Critique Agent: Another LLM (Grok-2) acts as a critique agent, analyzing the initial draft and providing feedback for improvement.
• Step 3: Revision: The writing agent (GPT-4o) receives the critique and the original product description to create a final, improved version of the marketing plan.
• This process involves three LLM calls compounded together, with each LLM playing a distinct role.

3. Code Example and Implementation

• The example is implemented in a Jupyter Notebook using Python.
• Libraries and API keys are loaded for accessing LLMs.
• The product description is defined: a system converting EHR data from FHIR to OMOP format.
• Writing Agent Implementation:
  • The OpenAI library is used to call the GPT-4o model.
  • A prompt is constructed, including the product description and a request to write a marketing plan draft.
  • The generated draft is saved to a PDF file.
• Critique Agent Implementation:
  • The XAI library is used to call the Grok-2 model.
  • A prompt is constructed, including the product description, the initial marketing plan draft, and a request for a critique.
  • The critique is saved to a PDF file.
• Revision Implementation:
  • The OpenAI library is used again to call the GPT-4o model.
  • A prompt is constructed, including the product description, the initial marketing plan draft, and the critique.
  • The model is asked to write a final version of the marketing plan.
  • The final plan is saved to a PDF file.

4. Analysis of the Marketing Plans

• The original and final marketing plans are compared.
• The final plan is noted to be longer and more detailed.
• Specific improvements include:
  • More detailed objectives with a long-term vision.
  • Added demographics to the target market section.
  • Added security and compliance angles to the unique selling proposition.
  • Expanded content marketing strategies to include interactive content and Q&A sessions.
  • Incorporated pay-per-click advertising and targeted advertisement concepts.
  • Added health IT organizations to strategic alliances.
  • Broadened metrics to include social media metrics.
  • Added elements to the conclusion about strategic partnerships.
• The budget in the final plan is slightly higher.
• The final plan is considered a "notch better" and worth the critique and revision process.

5. Implications and Use Cases of Compound LLMs

• Negative Implication: Increased processing time compared to a single LLM call. This makes it less suitable for chatbot-like experiences.
• Positive Implications:
  • Improved accuracy and performance, making it suitable for critical tasks like writing patient discharge summaries.
  • Ability to incorporate policy checks, ethics reviews, and brand alignment into the LLM workflow.
  • Creation of specialized agents for policy checking, ethics checking, and brand alignment.

6. Additional Agent Types

• The video suggests the possibility of incorporating other types of agents into the LLM workflow, such as:
  • Policy checking agent: Ensures the output aligns with organizational policies.
  • Ethics checking agent: Ensures the output adheres to ethical guidelines.
  • Brand agent: Ensures the output reflects the organization's brand identity.

7. Notable Quotes

• "You get a final product that's better than if you just called the LLM once."
• "Once you start chaining these LLMs together, you're usually talking about an experience that is not a chatbot."

8. Conclusion

• The video demonstrates how using multiple LLMs in a compound workflow can improve the quality and relevance of AI-generated content.
• While it increases processing time and cost, the benefits of improved accuracy, policy alignment, and brand consistency make it a valuable approach for specific use cases.
• The next video will explore a more complex version of this concept called "LLMs in a loop."