Building Multimodal Applications
Vision, audio, image generation and cross-modal AI systems
Build AI systems that see, hear and generate across modalities: vision-language models (OCR, chart reading, document AI), speech-to-text and text-to-speech (voice assistants), and image generation (DALL-E, Stable Diffusion). Includes production mitigations for VLM failure modes.
Who this series is for
This series is for engineers extending text-based LLM applications to images, documents and audio. It assumes comfort with LLM APIs and Python. Multimodal models are impressive in demos but have distinctive failure modes, such as misread numbers in charts, hallucinated text in scanned documents and transcription errors on domain vocabulary. The series treats those failure modes as first-class design concerns.
What each chapter covers
- Vision & Image Understanding covers vision-language models for OCR, chart and table reading and document AI, and when a traditional parser such as PyMuPDF is more reliable.
- Audio: Speech-to-Text & Text-to-Speech covers transcription with Whisper-class models, latency budgets for voice interfaces, and handling accents and domain terms.
- Image Generation & Cross-Modal covers image generation models, prompt control, and pipelines that move between modalities, such as describing an image and then generating a variation.
How to approach it
Collect a small set of your own difficult inputs (a dense table, a low-quality scan, a noisy recording) and run them through each technique as you read. Comparing outputs on hard cases shows you the limits far faster than clean examples. Expect about seven hours.
Skills you'll gain
Prerequisites
Chapters
How vision-language models see images: tokens, patches, resolution, OCR, chart reading and the failure modes of VLMs.
STT (Whisper, streaming ASR), TTS, voice-first applications and production audio pipelines.
DALL-E, Stable Diffusion, image-to-image, and cross-modal reasoning (text→image, image→text, image→audio).