Unlock the Power of AI Agents for Enhanced Outcomes
By Don Woodlock
AIBusinessTechnology
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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."
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