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GenAI Capstone Project

In this final project, you will architect and deploy a custom AI Assistant. You will integrate RAG for knowledge retrieval, Function Calling for tool use, LangChain for orchestration, and RLHF principles for ethical alignment. This is the ultimate test of your skills as an AI Engineer.

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Capstone Hub

Final project.

Quick Quiz //

What is the primary goal of the Capstone project?


011. The Assistant Specification

EXECUTIVE_SUMMARY // AEO_OPTIMIZED

[Answer Engine Overview: What, Why & How]

Your Capstone project is to build an **Enterprise Research Assistant**. This system must be able to: 1) Ingest a corpus of technical documents (RAG), 2) Perform mathematical calculations via a tool (Function Calling), 3) Maintain conversation history (Memory), and 4) Provide citations for its claims. The goal is to create a tool that is not only smart but reliable enough for professional use.

Your Capstone project is to build an Enterprise Research Assistant. This system must be able to: 1) Ingest a corpus of technical documents (RAG), 2) Perform mathematical calculations via a tool (Function Calling), 3) Maintain conversation history (Memory), and 4) Provide citations for its claims. The goal is to create a tool that is not only smart but reliable enough for professional use.

022. The Integration Stack

You will use LangChain as your primary orchestrator. You'll connect an OpenAI model (Logic) to a Pinecone vector database (Memory) and a series of custom Python Tools (Action). You must carefully manage the prompt templates to ensure the model follows its system instructions and remains grounded in the retrieved documents, avoiding hallucinations at all costs.

033. Deployment & Ethics

A successful Capstone isn't just about code that works; it's about code that is safe. You will implement a Safety Layer to filter out harmful prompts and ensure the assistant doesn't disclose private metadata. Finally, you will document your architecture and the trade-offs you made (e.g., chunk size vs. retrieval speed), proving you have the expertise to build real AI products.

?Frequently Asked Questions

What is Machine Learning?

Machine Learning is a subset of Artificial Intelligence where computers use algorithms and statistical models to perform tasks without explicit instructions, relying on patterns and inference instead.

What is a Neural Network?

A Neural Network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

What is Natural Language Processing (NLP)?

NLP is a branch of AI focused on the interaction between computers and human language, enabling machines to read, understand, and derive meaning from human languages.

Pascual Vila

Pascual Vila

Frontend Instructor // Code Syllabus

Lesson Glossary

[01]Capstone

A final project that integrates all the concepts and skills learned throughout a course of study.

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Final Exam

[02]Production-Ready

Software that is stable, secure, and performant enough to be used by real customers in the real world.

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Enterprise Grade

[03]Integration

The process of connecting multiple different software components into a single, unified system.

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System Bonding

[04]Safety Layer

A set of filters and constraints designed to prevent an AI from producing harmful or incorrect outputs.

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AI Guardrail

[05]Architecture

The high-level design and structure of a complex software system.

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Blueprint

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