Go beyond using ChatGPT. Learn to build LLM-powered products, fine-tune foundation models, architect RAG systems, and deploy GenAI into production. Two pathways. One outcome: becoming indispensable.
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Engineers who want to add LLM integration, RAG systems, and GenAI APIs to their skillset and move into AI engineering roles.
PMs who need deep technical fluency in GenAI to lead AI product teams, evaluate feasibility, and ship AI-powered features.
Professionals from any background ready to enter the fastest-growing tech sector — with starting salaries north of $120K.
Analysts and data scientists who want to transition from classical ML into the world of large language models and foundation models.
Consultants and strategists who need to understand and implement GenAI solutions for Fortune 500 clients and enterprise accounts.
Startup founders who want to build defensible GenAI products — not just API wrappers, but deeply engineered AI systems.
AI Startups · Big Tech · Consulting
OpenAI · Anthropic · Cohere · Startups
SaaS · Enterprise Software · FinTech
Legal Tech · Healthcare AI · Media
AWS · Microsoft · Google · Deloitte
DeepMind · Meta FAIR · Stability AI
Understand transformers, attention, tokenization, and how GPT-4, Claude, and Gemini actually work under the hood.
Zero-shot, few-shot, chain-of-thought, tree-of-thought — systematic prompting strategies that dramatically improve LLM output quality.
Build production RAG pipelines with vector databases, chunking strategies, reranking, and hybrid search.
LoRA, QLoRA, PEFT — fine-tune open-source models (LLaMA, Mistral) for custom domain tasks efficiently on consumer hardware.
Build complex LLM chains, memory systems, document loaders, and multi-step reasoning pipelines with LangChain and LlamaIndex.
Pinecone, Weaviate, Chroma — design, index, and query vector stores for semantic search and LLM memory systems.
Work with vision-language models, image generation (Stable Diffusion, DALL-E), and audio AI using GPT-4V and Gemini Vision.
Ship full-stack AI applications with Next.js, FastAPI, streaming responses, and real-time UIs powered by LLMs.
Evaluate LLM outputs, detect hallucinations, implement guardrails, and build responsible AI systems that meet enterprise standards.
Deep mathematical foundations of transformer architectures, scaling behavior, and emergent capabilities — the knowledge that separates LLM engineers from API consumers.
RAG-powered chatbot ingesting 10,000+ internal documents with hybrid search, reranking, and citation tracking.
Fine-tuned LLaMA model that extracts clauses, identifies risks, and summarizes contracts — deployed for a US law firm.
Vision-language pipeline that takes product images and generates SEO-optimized copy for e-commerce at scale.
Clinically-grounded Q&A with hallucination guardrails, source attribution, and confidence scoring for healthcare providers.
Text-to-SQL + data narrative generator that answers business questions in plain English using your company's database.
Build a complete GenAI-powered product from idea to deployed API — targeting a real problem in your industry.
Leads LLM fine-tuning and RAG modules. Shipped GPT-powered products to millions.
Leads RAG architecture and enterprise deployment modules. Designed the GenAI platform used by Fortune 100 clients at Microsoft.
Leads fine-tuning and safety modules. Works on Constitutional AI and model alignment at Anthropic.
Mentors retrieval and embeddings modules. Specialist in enterprise RAG systems.
Heads prompt design and evaluation modules. Builds Claude-powered workflows.
Leads model deployment modules. Maintains open-source LLM tooling.
I was a mid-level backend developer who had never worked with AI. The Generative AI program at Atlia was the most practical, rigorous thing I've ever done. Building the RAG system for the legal project was complex and real — exactly what my current employer tested me on in the technical interview. I joined Google's enterprise GenAI team two weeks after finishing.
I was a product manager who could barely read Python. After the GenAI PGP, I'm leading a team of 6 AI engineers at a Series B startup. The program gave me the technical depth to earn credibility with my team and the product insight to ship features that matter.
The Anthropic mentor was worth the entire program fee alone. Having someone explain Constitutional AI and real alignment challenges from inside the industry changed how I think about building AI products. Currently consulting for a $2B hedge fund on their GenAI strategy.
I was a front-end dev going nowhere. Building a full RAG application in the program became my portfolio centerpiece — Notion hired me to work on their AI features.
As a copywriter, prompt engineering clicked instantly. Atlia taught me the systems side — evaluation, RAG, fine-tuning — that turned a knack into a real engineering role.
I went from manual QA to building LLM-powered features. The hands-on projects with LangChain and vector databases made all the difference in my interviews.
I moved from product management into actually building. The RAG and fine-tuning projects gave me hands-on credibility — Shopify hired me to lead an AI commerce feature.
Book a free 30-minute counselling session. We'll help you pick between PCP and PGP and build your personal roadmap.