Multimodal AI

Work with images, audio, and video using AI models

Understand how vision-language models process images and generate descriptions

Build applications that analyze images: OCR, object detection, scene understanding

Master 5 prompt strategies for vision models: from generic descriptions to structured JSON and targeted audits

Learn to extract structured data from document scans: receipts, invoices, contracts — with validation and confidence markers

Learn 5 types of vision hallucinations (object, attribute, spatial, OCR, counting) and strategies to detect and prevent each one

Learn 3 architectures of multimodal RAG: CLIP embeddings, LLM-generated summaries, and ColPali — when to use which approach

Create voice-based AI assistants using speech-to-text, LLMs, and text-to-speech

Compare traditional pipelines (STT→LLM→TTS) vs end-to-end models (GPT-4o): latency, voice preservation, interruptions, and voice+vision

Explore video understanding, audio analysis, and multimodal content generation

Calculate vision API costs: how resolution affects tokens, provider comparison, and cost optimization for images and video