Generative AI
In Production.

Get your development environment ready with just the essential tools and libraries you need.

Learn the fundamental AI and LLM concepts required for building production applications.

Understand the problem space and requirements for building reliable LLM applications.

Define clear requirements and constraints for the system.

What are the main components of a LLM application?

Master Retrieval-Augmented Generation (RAG), the game-changing technique that enhances LLM responses with real-world knowledge

Deep dive into BM25 and key metrics to measure and optimize your retrieval system's effectiveness

Leverage advanced re-ranking techniques to dramatically improve search relevance and accuracy

Harness the power of vector embeddings to capture semantic meaning and revolutionize your search capabilities

Create a best-of-both-worlds search system by masterfully combining RAG with re-ranking strategies

Put it all together to build a production-ready RAG system that delivers accurate, context-aware responses

Set up a complete Python development environment with VS Code, uv, OpenAI API access, FastAPI, and essential libraries for multimodal AI development

Master fundamental concepts like image embeddings, vision-language models, prompt engineering, and vector databases essential for multimodal AI

Learn to build an AI shopping assistant that helps users find products, answer questions, and compare items with high accuracy and speed

Implement advanced image processing and understanding capabilities using Vision-Language Models and embeddings

Build robust query understanding systems that can interpret and process user questions about visual products

Optimize your multimodal QA system to achieve sub-2-second response times while maintaining 95%+ accuracy

Learn the fundamentals of Vision Language Models and how they can be used to extract structured information from images and documents

Implement information extraction pipelines using GPT-4V, focusing on prompt engineering and output formatting for reliable data extraction

Explore open source VLMs like LLaVA and CogVLM for information extraction tasks, with practical deployment considerations

Build an end-to-end document processing pipeline combining OCR, VLMs, and post-processing for robust information extraction

Advanced techniques for extracting information from complex documents like invoices, receipts, and forms using VLMs

Implement validation rules and error handling mechanisms to ensure accurate and reliable information extraction

Understand the core components of voice AI systems including speech recognition (ASR), text-to-speech (TTS), and voice activity detection

Implement real-time speech recognition using OpenAI's Whisper model, handling streaming audio and processing continuous speech

Integrate Google's Gemini API for advanced voice capabilities, including multilingual support and speaker diarization

Design and implement real-time voice processing pipelines with WebRTC and WebAudio APIs for low-latency applications

Learn best practices for voice user interface design, including conversation design, error handling, and user feedback

Deploy voice applications at scale, handling concurrent users, optimizing latency, and managing cloud resources efficiently