Intermediate Free 120 min total Course 13 of 90

Multimodal AI:
Text, Vision, and Audio

Your users don't communicate in text alone. They send screenshots, invoices, voice notes, and PDFs. Modern AI systems process all of it together. This course explains how vision-language models work, how document AI extracts structure from unstructured files, how audio pipelines handle speech and sound, and how you build enterprise systems that reason across all three modalities at once.

Start Module 1 → Talk to Arjun
Is this course for you?
This IS for you if
  • You build AI features that involve images, documents, PDFs, or audio
  • You are evaluating vision-language models or document AI vendors
  • You want to understand how GPT-4o, Gemini, and similar models handle non-text inputs
  • You need to process invoices, contracts, or scanned forms at scale
  • You are building a voice interface or transcription pipeline
This is NOT for you if
  • You have never used an LLM API (start with the Prompt Engineering course first)
  • You are looking for computer vision from scratch, image classification, or object detection without language models
  • You need signal processing or audio engineering beyond AI inference
ⓘ
Prerequisites: Comfort with calling an LLM API in Python or similar. The Prompt Engineering and RAG courses cover the foundations this course builds on. You do not need a computer vision or audio background.
What you will be able to do
✓Explain how vision-language models encode and align image and text representations
✓Build a document AI pipeline that extracts structured data from PDFs, invoices, and scanned forms
✓Assemble a speech-to-insight pipeline covering transcription, speaker diarization, and language model analysis
✓Implement cross-modal retrieval so users can search text with images and vice versa
✓Design a multimodal agent that chooses which modality to query based on the task at hand
✓Evaluate multimodal systems for accuracy, latency, cost, and enterprise compliance requirements

The Six Modules

01
How Vision-Language Models Work
What happens when an image enters a language model. Patch embeddings, cross-attention, contrastive pretraining (CLIP), and why these models can describe, reason, and hallucinate about what they see.
20 min
→ 02
Document AI: PDFs, Invoices, and Forms
OCR is not enough. How modern document AI combines layout detection, table extraction, and language model reasoning to turn unstructured documents into structured data your systems can act on.
20 min
→ 03
Audio AI: Transcription, Diarization, and Analysis
From raw audio to structured insight. How Whisper-class models transcribe speech, how speaker diarization works, and how to pipe transcripts into language models for summarization and action extraction.
20 min
→ 04
Cross-Modal Retrieval
How to build a search system where a text query returns images and an image query returns text. Shared embedding spaces, CLIP-based retrieval, multimodal vector stores, and when this beats text-only RAG.
20 min
→ 05
Multimodal Agents
What changes when an agent can see, listen, and read. Tool routing across modalities, state management when inputs arrive in different formats, and real enterprise architectures for multimodal agentic workflows.
20 min
→ 06
Evaluating and Deploying Multimodal Systems
How to measure what matters: accuracy on your document types, latency under load, cost per modality, and data residency. Build/buy/configure decisions, compliance considerations, and the production readiness checklist.
20 min
→ CA
Capstone: Build a Multimodal Document Intelligence System
End-to-end project: ingest a PDF invoice, extract line items with document AI, pull in an audio meeting summary, and use a vision-language model to reconcile discrepancies. Full working code included.
30 min
→

Related Courses

Course 02
Retrieval-Augmented Generation
Course 05
AI Agents
Course 07
Evaluating AI Systems

Excited about AI, innovation, and growth?

Start a conversation