GenAI Engineer Project - Saskia Katharina Sack

GenAI Engineer Project - Saskia Katharina Sack

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This track equips learners to proficiently create, integrate, test, and deploy AI-driven applications and prompt engineering solutions, leveraging modern AI models and real-life cloud environments. This is a back-end oriented project.
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Project Definition
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Project Links
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Project Resources
Written ResourcesVideo Resources
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MVP
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Timeline
Week
Topic
Project Tasks
Technology / Methods
Completed
1
Define project idea and get it approved by Role Expert by EOW.
2
Backend
- setup a server - setup a database - setup a GitHub repository and connect the project to it. add Git Ignore. - Create an ENV file to store sensitive data (database API Key) - connect server to database - create first API endpoints to test all CRUD operations for one collection in the database. - Use PyLint to ensure a high level of code quality. - Use prettier to ensure alignment in code formatting.
3
AI
- Proof of Concept with LLM - Create a comparison table between the LLMs in our case study and reach a recommendation of what to use. - Define a UML/diagram of the flow of prompts. - Choosing the Gen stack (frameworks, libraries).
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MVP requirements
  • AI Model Integration: Integrate pre-trained models (e.g., GPT-3) via LangChain.
  • Data Handling: Create a pipeline for data preprocessing, cleaning, and preparation.
  • Prompt Engineering: Design effective prompts for various use cases using LangChain.
  • API Development: Build APIs to integrate AI models (FastAPI / Flask).
  • Database: Use a database (e.g., PostgreSQL) to store prompts, user data, and responses.
  • Validation and Security: Validate inputs and sanitize data to prevent injection attacks.
  • Deployment: Deploy the app on cloud platforms (Vercel / Render) with ENV variables, repo, git, build pipeline.
  • No UI: Focus on backend functionalities only.
  • Authentication - Minimal version (Firebase).
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V2

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V2 optional requirements
  • Testing
  • Data Preprocessing, Embeddings.
  • Integrations
    • Connect GPT with external end-point that we created
    • Connect GPT to Database
  • Authentication: Implement authentication using JWT. (Moved here by Alon)
  • Caching, Token Optimization
  • UI