There is a difference between understanding AI and being able to use it when it matters. Most courses stop at the first. We care about the second, patiently turning what you learn into real skills and real project work, so that when the moment comes to interview at Bonami Software, you can show, with quiet confidence, exactly what you are capable of.
Unlike programs that hand you theory and a certificate, ours combines theory, practical skills, a real-world project and a recognised credential. You work with the same AI technologies companies use today, so by the time you reach an interview you can demonstrate what you have built, not just describe what you have read about.
AI is not a side module here. It is the entire program, woven through everything you do in every lesson.
Five modules that build steadily, each one resting on the last, so you grow from understanding models to deploying your own AI application.
Before you build with AI, you come to understand it from the inside. We cover transformers and attention, tokens and embeddings, how models are trained, context windows, sampling, and why models behave, and misbehave, the way they do. You finish with a grasp of LLMs that many working engineers never acquire.
You learn to communicate with models precisely: prompt patterns, chain-of-thought, few-shot, structured outputs, and tool calling. You work hands-on with the OpenAI and Anthropic Claude APIs, and learn to manage cost, latency and reliability like a professional.
You build retrieval-augmented systems that ground AI in real knowledge: RAG pipelines, GraphRAG and hybrid search, using vector stores such as Pinecone, Weaviate, ChromaDB, FAISS and pgvector, and memory layers like Graphiti, mem0 and Zep.
This is where AI starts to act, not just answer. You build agents and multi-agent systems with LangChain, LangGraph, CrewAI and AutoGen, handling tool use, memory and planning, and working with the OpenAI Agents SDK, Google ADK and the Model Context Protocol (MCP).
You bring everything together into a real, deployed product: full-stack GenAI architecture, streaming, guardrails, evaluation and observability, and deployment with FastAPI, Streamlit and Docker, with a close eye on cost and latency. You finish with a working AI application you built yourself, ready for your portfolio.
Real proficiency you can point to in an interview, across every layer of modern AI work.
| Skill Area | Proficiency After the Program |
|---|---|
| Understanding How LLMs Work | Advanced |
| Prompt Engineering & LLM APIs | Advanced |
| RAG & Knowledge Systems | Intermediate to Advanced |
| AI Agents & Orchestration | Intermediate to Advanced |
| Building & Deploying GenAI Apps | Intermediate to Advanced |
The same tools working AI teams reach for every day, grouped by the part of the program where they come alive.
Model APIs
Agents & Orchestration
Vector Stores
Memory Layers
Build & Deploy
You do not need a deep background in mathematics or computer science to begin. The program starts gently, with how LLMs actually work, and builds steadily toward retrieval systems, agents and deployment. A little comfort with technology helps, and a willingness to show up and build matters most. Everything else, we teach you, patiently and in person.
Seats are limited and every batch is kept small, so each learner is truly seen. If you are ready to turn what you learn into real skill, this is where it starts.