Generative AI Systems Engineering: Build Copilots, Multi-Model Pipelines & LLM
Data Science Academy
Build Production-Ready AI Systems
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7 people enrolled last week.
Learn to Build Production-Ready Generative AI Systems & LLM Applications
Generative AI has moved far beyond simple prompts and chatbots. Companies now need engineers and developers who can build complete AI systems — applications that combine multiple models, APIs, reasoning pipelines, memory, tools, and external data.
However, most courses only teach prompt engineering or isolated API usage. They rarely show how real AI systems are designed, orchestrated, evaluated, and deployed in production environments.
This course closes that gap.
You will learn how modern LLM ecosystems work across OpenAI, Anthropic, and Mistral, and how to build real applications using their APIs. Instead of theory alone, we focus on practical system design, including structured outputs, function calling, reasoning workflows, multi-model orchestration, and retrieval-augmented generation (RAG).
Through hands-on labs and projects, you will build real AI utilities such as copilots, API-driven applications, hybrid model pipelines, and knowledge-aware assistants.
By the end of this program, you will move beyond basic prompting and gain the skills needed to design, build, and deploy modern generative AI systems used by real products and companies today.
What you’ll learn
Move from basic prompting to building production-ready AI systems using GPT, Claude, Mistral APIs with copilots, RAG, model orchestration
Understand LLM architectures, tokens, context windows, and parameters affecting output quality
Compare GPT, Claude, and Mistral ecosystems and choose the right model for each task
Configure APIs, SDKs, and prompts to build reliable AI-powered workflows
Design high-impact prompts using role prompting, templates, and few-shot examples
Control style, tone, and instructions for consistent AI outputs
Optimize prompts through debugging, iteration, and prompt compression techniques
Implement JSON-based responses and schema validation for reliable outputs
Build multi-tool workflows using function calling and dynamic inputs
Create AI tools like weather bots and task planners using real APIs
Build copilots using FastAPI, Flask, Streamlit, or React interfaces
Implement streaming responses and context persistence for AI assistants
Design applications like travel planners and code review copilots
Route tasks across GPT, Claude, and Mistral based on strengths
Build sequential and parallel AI pipelines for complex workflows
Apply model voting, fallback logic, and cost optimization strategies
Build retrieval pipelines using vector databases like Pinecone and FAISS
Connect AI systems to real-time APIs and external data sources
Implement evaluation dashboards, guardrails, and monitoring for production
Learn directly from Data
Data Science Academy
AI educator helping engineers build real-world generative AI systems
Who this course is for
Developers who want to build real LLM applications, AI copilots, and multi-model systems using GPT, Claude, Mistral, and modern APIs.
Engineers looking to integrate generative AI into products using APIs, function calling, RAG pipelines, and production AI workflows.
Product builders and startup founders who want to design and launch AI-powered tools, copilots, and intelligent automation systems.
What's included
Live sessions
Learn directly from Data Science Academy in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
Week 1
Day 1 - Foundations of Generative AI Systems
Day 2 - Mastering Prompt Engineering & Context
Day 3 - Working with GPT, Claude & Mistral APIs
Day 4 - Function Calling & Structured Outputs
Day 5 - Reasoning, Chain of Thought (CoT) & JSON Mode
Day 6 - Building Real-World AI Utilities & Copilots
Day 7 - Multi-Model Orchestration & Hybrid AI Systems
Week 2
Day 8 - Integrating Memory, Tools, and External Data
Day 9 - Evaluation, Safety, and Deployment
Schedule
Live sessions
2 hrs / week
Projects
11 hrs / week
Async content
15 hrs / week
Frequently asked questions
$200
USD