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You will learn to:
Build AI-driven workflows by combining multiple language and vision models.
Apply prompt engineering techniques to solve diverse natural language tasks.
Use the OpenRouter API to query hosted DeepSeek models from Python.
Design reusable, multi-step pipelines that process, generate, and transform content.
Skills
API Integration
Generative AI
Large Language Models (LLMs)
Prerequisites
Intermediate knowledge with Python syntax
Understanding of APIs and HTTP requests
Familiarity with generative AI concepts
Familiarity with prompt engineering principles
Basic understanding of large language models (LLMs)
Technologies
Python
Project Description
In this project, we’ll build a hands-on Python notebook that integrates DeepSeek models using the OpenRouter API. We’ll work on real-world tasks such as summarizing research papers, writing customer support emails, generating Instagram captions, and solving coding problems, each powered by different DeepSeek models. We’ll use models like deepseek-r1, deepseek-v3-base, and deepseek-r1-0528 to generate language and code within a Jupyter Notebook.
This project introduces learners to prompt engineering, token counting, model selection, and multi-model chaining, key concepts in modern AI application development.
Project Tasks
1
Introduction
Task 0: Get an Overview
Task 1: Import Libraries
Task 2: Set Up the API Key and Endpoint
2
Email Generation
Task 3: Generate Emails for Reviews
3
Code Generation
Task 4: Generate the Python Code
4
Text Summarization
Task 5: Summarize Text
5
Image Caption Generation
Task 6: Generate Instagram Captions
Congratulations!
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Atabek BEKENOV
Senior Software Engineer
Pradip Pariyar
Senior Software Engineer
Renzo Scriber
Senior Software Engineer
Vasiliki Nikolaidi
Senior Software Engineer
Juan Carlos Valerio Arrieta
Senior Software Engineer
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.