Saturday, 21 March 2026

Full stack generative and Agentic AI with python

Python Developer March 21, 2026 Generative AI, Python No comments

Introduction

Generative AI and agentic systems represent the frontier of artificial intelligence today — not just models that respond to prompts, but systems that reason, act, collaborate and build applications end-to-end. The course “Full stack generative and Agentic AI with python” is designed to take you from the ground up: from Python fundamentals through to building full-scale, production-ready AI applications involving LLMs, RAG (Retrieval-Augmented Generation), vector databases, prompt engineering, multi-modal agents, memory systems and deployment workflows. If you’re looking to become an AI engineer in the modern sense — not just training models, but deploying intelligent systems — this course aims to deliver that.

Why This Course Matters

Complete skill spectrum: It doesn’t stop at “generate text” or “use embeddings” — it covers Python programming, system tools (Git, Docker), prompt design, agent frameworks, memory & graph systems, multi-modal input and deployment. This breadth prepares you for real-world AI engineering.
Industry relevance: With large language models (LLMs) and agentic workflows dominating AI job descriptions, knowing how to build these from scratch gives you a competitive edge.
Hands-on and applied: Rather than just theory, the course emphasises building real applications: agents that use memory, vector-DBs, processing of voice/image/text, deploying services.
End-to-end mindset: From code and data to deployment and system scaling, the course helps you see the full lifecycle of AI applications — which is often missing in many shorter courses.

What You’ll Learn

Here’s a breakdown of major topics in the course and what you’ll gain at each stage.

Foundations: Python, Git & Docker

You’ll review or learn Python programming from scratch: syntax, data types, object-oriented programming, asynchronous programming, modules and packages.
Git and GitHub workflows: branching, merging, collaboration, version control for AI projects.
Docker containerization: how to package AI apps, manage dependencies, build services that can be deployed to production.

AI Fundamentals: LLMs, Tokenization & Transformers

What makes a large language model (LLM) tick: tokenization, embeddings, attention mechanism, transformer architectures.
Practical setup: integrating with model APIs (e.g., OpenAI, Gemini) and local model deployments (e.g., Ollama, Hugging Face).
Prompt engineering: crafting zero-shot, few-shot, chain-of-thought, persona-based and structured prompts; encoding outputs with Pydantic for type-safe APIs.

Retrieval-Augmented Generation (RAG) & Vector Databases

Indexing, embedding, and retrieving documents from vector stores to supplement LLMs with external context.
Building end-to-end pipelines: document loaders, chunking, embedding, vector DB (e.g., Redis, Pinecone, etc.).
Deploying the RAG service: backing it with APIs, scaling retrieval, using queues/workers to support asynchronous workflows.

Agentic AI & Memory Systems

Building agents that can act, maintain memory and state, interact with environments or external tools.
Memory architectures: short-term, long-term, semantic memory; building graph-based memory with Neo4j or similar.
Multi-agent orchestration: using frameworks like LangChain, LangGraph, Agentic protocols (MCP) and designing workflows where agents collaborate, plan, sequence tasks.

Multi-Modal & Conversational AI

Extending beyond text: integrating speech-to-text (STT), text-to-speech (TTS), image inputs and multimodal models.
Building voice assistants, conversational agents, multi-modal workflows that can interact via voice, chat and images.
Deploying these services using FastAPI or other web frameworks, serving models via APIs.

Deployment, Scaling & Production Practices

Packaging AI applications with Docker, deploying via APIs, monitoring and logging, versioning models.
Scaling considerations: asynchronous job queues, worker architectures, vector DB scaling, agent orchestration in production.
System design: how to structure a full AI system (frontend, backend, model services, memory/store layers) and maintain it.

Real-World Projects

The curriculum includes a series of hands-on projects, e.g., building a tokenizer from scratch, deploying a local LLM app via Docker + Ollama, creating a RAG system with vector DB and LangChain, building a voice-based agent, implementing graph-based memory in an agent, etc.
By working through these, you’ll build a portfolio of applications, not just scripts.

Who Should Take This Course?

Developers, engineers or data scientists who already know some Python (or are willing to learn) and want to move into the domain of full-stack AI engineering.
Backend or systems engineers interested in integrating AI into services and apps—building not just models but systems.
Anyone aiming to build AI agents, deploy LLMs, build RAG systems, and develop production-ready AI applications.
Students or career-changers who want a comprehensive, modern path into AI engineering (not just ML).

If you're brand new to programming or AI, the pace may be challenging—especially in later modules covering agentic architectures and deployment. But the course starts from basics, which is helpful.

How to Get the Most Out of It

Code as you go: Every time you see a code example, type it out, run it, tweak it. Change dataset or prompt parameters and see the effects.
Build your own mini-projects: After finishing core modules, pick an application of your interest (e.g., a voice assistant for your domain, a knowledge-agent for your documents, a vector DB-powered search chat) and build it using the frameworks taught.
Document your work: Keep notebooks or scripts with comments, write short summaries of results, what you changed, why you changed it. This builds your portfolio.
Experiment with architecture: Don’t just stick to the given design—modify agent memory, add multi-modal inputs, try different vector stores or prompt designs.
Deploy and monitor: Try deploying a model/service (e.g., in Docker) and experiment with latency, scale, concurrency, memory store behavior.
Reflect on trade-offs: When building RAG or agents, think: what are the memory and compute costs? What are failure modes? How could I secure the system?
Stay current: Generative & agentic AI is evolving rapidly—use the course as base but explore new frameworks/tools as you go (LangGraph, CrewAI, AutoGen etc).

What You’ll Walk Away With

By the end of the course you should be able to:

Write full-stack Python applications that integrate LLMs, vector databases, and agentic workflows.
Understand and implement prompt engineering, retrieval-augmented generation (RAG), multi-modal inputs (text, voice, image) and agent memory systems.
Deploy AI services using Docker, manage versioning, monitor systems, and think about scale.
Build a portfolio of real applications (tokenizer, RAG chat, voice assistant, memory-graph agent) that demonstrate your practical skills.
Be prepared for roles such as AI Engineer, LLM Engineer, Agentic AI Developer, or backend engineer working with AI systems.

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Conclusion

The “Full stack generative and Agentic AI with Python” course is a strong choice if you’re serious about building not just models, but full-scale AI systems. It offers a modern, comprehensive path into AI engineering: from Python fundamentals to LLMs, RAG, agents, memory and deployment. If you commit to the hands-on work, build projects, and integrate what you learn, you’ll leave with both knowledge and demonstrable skills.