AI TRAINING
Multimodal AI Engineering for Product Teams
Build production systems that combine vision, text, and audio using state-of-the-art multimodal LLMs.
What it covers
This practitioner-level programme equips engineers and product teams with the skills to design, build, and deploy multimodal AI systems that integrate vision, text, and audio inputs. Participants work hands-on with leading multimodal models—including GPT-4o, Claude Vision, and Gemini—and learn how to select the right architecture for real-world use cases such as document intelligence, visual Q&A, and audio transcription pipelines. The course covers prompt engineering for multimodal inputs, grounding techniques, output parsing, and evaluation strategies specific to non-text modalities. Format combines live coding sessions, guided labs, and architecture review workshops.
What you'll be able to do
- Send structured multimodal prompts (image + text) to GPT-4o, Claude Vision, and Gemini and parse their outputs reliably in production code
- Design an end-to-end document intelligence pipeline combining OCR, layout detection, and visual Q&A
- Build an audio processing workflow that transcribes, diarises, and routes spoken input to downstream actions
- Select the appropriate multimodal model and architecture for a given cost, latency, and accuracy constraint
- Evaluate multimodal system quality using task-specific metrics beyond simple accuracy
Topics covered
- Architecture of multimodal LLMs: how vision, text and audio encoders are unified
- Hands-on with GPT-4o, Claude Vision, and Gemini multimodal APIs
- Prompt engineering and grounding for image and audio inputs
- Document intelligence: OCR, layout parsing, and visual Q&A
- Audio pipelines: transcription, speaker diarisation, and speech-to-action
- Evaluation and benchmarking of multimodal outputs
- Cost, latency, and reliability trade-offs across providers
- Production patterns: batching, caching, and error handling for multimodal workloads
Delivery
Delivered over 3-5 days, either in-person or fully remote via video conference with shared cloud lab environments. Each module is approximately 60% hands-on coding and 40% instructor-led explanation and architecture discussion. Participants receive access to a shared GPU-enabled cloud workspace pre-loaded with API keys and starter notebooks. A private Slack or Teams channel is maintained for async support during and after the bootcamp. In-person delivery is available in major European cities; remote cohorts are capped at 16 participants to preserve interactivity.
What makes it work
- Start with a single, well-scoped multimodal use case (e.g., invoice extraction) before generalising the architecture
- Build a dedicated evaluation dataset with labelled multimodal examples before going to production
- Assign an engineer with API integration experience as the internal champion to maintain provider SDK updates
- Instrument cost and token usage per modality from day one to avoid bill shock at scale
Common mistakes
- Treating multimodal prompting identically to text prompting — image and audio tokens have different cost structures and context window implications that catch teams off guard in production
- Skipping modality-specific evaluation: teams ship visual Q&A features without benchmarks and only discover failure modes after user complaints
- Picking a single provider without testing latency and accuracy trade-offs across GPT-4o, Gemini, and Claude for the specific use case
- Underestimating preprocessing complexity — raw images and audio almost always need normalisation, resizing, or chunking before model ingestion
When NOT to take this
This training is not the right fit for a team that has not yet shipped any LLM-based feature to production — they will struggle with the hands-on labs and would benefit more from a foundational LLM engineering workshop first.
Providers to consider
Sources
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