Course Objective & Scope
In real business workflows users upload photos, leave voice notes and attach PDFs at the same time; combining those inputs into one reliable application requires proper media preprocessing, schema design and error handling. This programme covers the architecture of production-ready applications built on multimodal APIs.
Participants implement image resizing, audio chunking, multimodal prompting and structured output extraction with JSON Schema, building a working prototype and a latency and quality measurement plan on an organisational case such as field claim reporting or customer onboarding.
Who Should Attend?
- Software and AI engineers building multimodal features
- Solution architects designing enterprise AI applications
- Technical product managers and rapid prototyping teams
- Prerequisite: Python or TypeScript and basic LLM API usage
Key Takeaways
- Design architectures that combine image, audio and text inputs in a unified API flow
- Reduce token cost through media compression, cropping and frame sampling
- Extract schema-validated structured data from multimodal inputs
- Build fallback and human review paths for low-confidence or corrupted media
- Prepare an end-to-end latency and quality measurement plan for the application
Curriculum
Multimodal System Architecture
- Synchronous versus asynchronous media processing pipelines
- Object storage, caching and API payload management
Media Preprocessing and Token Economics
- Image resolution/tiling, PDF page rasterisation and audio codecs
- Preprocessing rules that cut latency and cost without hurting accuracy
Multimodal Prompting and Structured Output
- Aligning image, text and audio context in a single request
- Extracting structured data from multimodal inputs with Pydantic / JSON Schema
Model Routing and Error Handling
- Cascaded routing between fast/lightweight and frontier models
- Validation and fallback paths for blurry images, noisy audio and missing pages
Case Lab and Measurement Plan
- Multimodal intake/inspection prototype on an organisational case
- Latency budget and quality measurement plan
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