ExplainLLM LogoExplainLLM

Multimodal AI

Work with images, audio, and video using AI models

1
Vision LLMs
GPT-4V, Claude Vision

Understand how vision-language models process images and generate descriptions

2
Image Analysis
Practical applications

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

3
Prompt Engineering for Vision
One image — five results

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

4
Document Understanding
From scan to structured data

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

5
Vision Hallucinations
When models lie with confidence

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

6
Multimodal RAG
Search across text and images

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

7
Voice Agents
Whisper + TTS + LLM

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

8
Real-Time Multimodal
300ms instead of 1 second

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

9
Video & Audio
Emerging capabilities

Explore video understanding, audio analysis, and multimodal content generation

10
Multimodal Costs
How much does one image cost?

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