Build complete AI products from idea to production. Master RAG architecture, vector databases, AI API design, frontend integration, scaling, and the business strategy to bring AI products to market.
Identify high-value AI product opportunities. User research for AI features, designing with AI limitations in mind, prototyping AI experiences, and validating product-market fit.
Design production-grade RAG systems. Document processing pipelines, hybrid search (keyword + semantic), re-ranking, query routing, and advanced retrieval strategies for accurate results.
Master vector databases: Pinecone, Weaviate, and Chroma. Indexing strategies, metadata filtering, hybrid queries, scaling considerations, and choosing the right database for your use case.
Design and build robust AI-powered APIs with FastAPI. Streaming responses, async processing, request queuing, error handling, and API versioning for AI endpoints.
Connect AI backends to modern frontends. Streaming UI patterns, typewriter effects, conversation management, file upload handling, and responsive AI chat interfaces.
Secure your AI product. JWT authentication, API key management, usage-based rate limiting, token counting, cost attribution per user, and preventing prompt injection attacks.
Monitor AI products in production. LLM tracing (LangSmith, Langfuse), latency tracking, quality scoring, user feedback loops, and automated regression detection.
Scale AI products efficiently. Caching strategies, model routing (small vs. large models), batch processing, auto-scaling, and keeping inference costs under control as you grow.
Bring your AI product to market. Pricing models for AI products (per-token, per-seat, usage-based), beta programmes, gathering user feedback, and iterating based on production data.
Engineers and developers who want to build and ship their own AI-powered products
Technical founders and CTOs building AI-first startups or adding AI features to existing products
Product managers who need deep technical understanding of AI product architecture and constraints
Programming experience and basic ML knowledge required. You should be comfortable building web applications (any framework), understand APIs, and have basic familiarity with ML concepts. Our Prompt Engineering Pro course pairs well as preparation.
Interactive sessions with real-time Q&A and screen sharing
All sessions recorded and available for 12 months after the course
Real-world projects that build your portfolio as you learn
Personal mentoring sessions to address your specific questions
Industry-recognised certificate upon successful completion
Our instructors are seasoned practitioners with years of experience building production AI systems. They hold certifications across major cloud platforms and have trained thousands of professionals worldwide.
Yes. The course is structured around building a single AI product from start to finish. By week 8 you will have a deployed, production-ready AI application with authentication, monitoring, and a launch-ready landing page.
FastAPI (Python) for the backend, Next.js or React for the frontend, PostgreSQL + a vector database for storage, and your choice of LLM provider. The architecture patterns work with any stack.
Primarily technical (70%) with essential business strategy (30%). You will write code every week, but also learn pricing, positioning, and launch strategies that are critical for AI product success.
Absolutely. We encourage you to apply the course structure to your own product idea. The instructors provide personalised feedback on your specific architecture and product decisions.