Capstone Project: The AI Architect

Congratulations! You have completed all 12 modules of the Understanding Large Language Models course. You have evolved from a curious observer to a knowledgeable architect of the AI age.

To earn your metaphorical "Degree," you must now complete your final project. This isn't a coding test; it's an Architecture Design Challenge.

The Challenge: The Digital Doctor Assistant

Scenario: A major hospital wants to build an AI assistant to help doctors summarize patient records and suggest potential diagnoses. This is a high-stakes environment where errors (Hallucinations) are dangerous and privacy is paramount.

Your Task:

You must design the system using the components we've studied. Answer the following 6 questions based on your course knowledge:

Step 1: Data and Privacy (Module 2 & 12)

Doctors will be uploading handwritten notes and voice recordings.

Question: Will you use a standard LLM or a Multimodal model?
Privacy: How will you ensure patient data never leaves the hospital's secure network?

Step 2: The Logic Engine (Module 5 & 6)

You need to decide how the model generates its advice.

Question: Will you use a high or low Temperature? Why?
Logic: Will you allow the model to answer directly, or will you force it to use Chain-of-Thought reasoning to show its diagnostics steps?

Step 3: Reliability and Facts (Module 3 & 7)

The model must not guess a patient's history.

Question: Explain how you will use RAG (Retrieval-Augmented Generation) to connect the AI to the hospital's central patient database.
Safety: What "System Instruction" will you give the AI to prevent it from hallucinating a diagnosis?

Step 4: Customization (Module 9)

Doctors use very specific medical jargon that general models often misunderstand.

Question: Will you perform Full Fine-Tuning or use a LoRA Adapter? Explain your choice based on cost and computing power.

Step 5: Safety and Bias (Module 8)

Studies show that medical history can have racial and gender biases.

Question: How will you use Safety Guardrails to scan the AI's output for potential bias before the doctor sees it?

Step 6: Orchestration (Module 10 & 11)

The doctor wants the AI to not just "Talk," but to automatically book follow-up appointments.

Question: Explain how Function Calling will allow the AI to interact with the hospital's calendar system.

Final Project Submission

Create a GitHub Gist or a short document outlining your "Architecture Map." You should be able to justify every choice using the technical terms we covered (Tokens, Embeddings, Attention, Temperature, Alignment, etc.).

graph TD
    Doctor["Doctor Query"] --> Router["Agentic Router (Module 10)"]
    Router --> RAG["RAG Search: Patient Records (Module 7)"]
    Router --> Prompt["CoT Prompting (Module 10)"]
    RAG & Prompt --> Brain["Fine-Tuned LMM (Module 9 & 12)"]
    Brain --> Guard["Safety Filter (Module 8)"]
    Guard --> Result["Final Diagnosis Suggestion"]
    Result --> Action["Function Call: Book Appointment (Module 10)"]

Final Closing Thoughts

The journey doesn't end here. The world of Large Language Models changes every week. You now have the foundation to understand those changes as they happen.

Thank you for being part of this course. Go forth and build something that makes the world a little smarter, safer, and more helpful!

Course Completed.