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Learn · 01 of 06 · Multimodality in Generative AI
Multimodality in Generative AI
Multimodality in Generative AI 01/06 · Introduction Next The real multimodality problem Multimodality in Generative AI → Library 01/06 · Multimodality in Generative AI → 01 Introduction 02 The real multimodality problem 03 Alignment 04 Architectures 05 Evaluation 06 Risks 01 AI and Generative AI Foundations Series · 5 items 02 From the Caves to AGI Series · 6 items 03 Multimodality in Generative AI Series · 6 items 04 Reasoning Models Series · 6 items 05 AI, GDP, Well-being and Energy Series · 5 items 06 Data Centers in Space Series · 5 items 07 AI Security Series · 6 items 08 AI Agents Series · 6 items 09 Realtime Voice Agents Series · 6 items 10 Coding Agents & Agent Harnesses Series · 6 items 11 Context Engineering, Memory & MCP Series · 6 items 12 LLM Inference Engineering & Economics Series · 6 items 13 Evaluating AI Systems in Production Series · 6 items 14 Technical notes Build · 3 items
01 Context engineering vs prompt engineering Open 02 Context budgets, prioritization, compaction, and provenance Open 03 Memory architectures: working, episodic, semantic, and persistent state Open 04 Retrieval and context assembly Open 05 MCP hosts, clients, servers, tools, resources, prompts, lifecycle, and trust boundaries Open 06 Skills, plugins, subagents, and hooks Open 01 Prefill vs decode Open 02 KV cache, memory hierarchy, continuous batching and PagedAttention Open 03 Quantization, parallelism, and memory/performance/quality trade-offs Open 04 Speculative decoding, prefix caching, and other latency optimizations Open 05 Model routing, fallback, caching, and workload-aware serving Open 06 Benchmarking inference: cost/task, throughput, latency, energy, and hardware constraints Open 01 What to evaluate: model, component, system, workflow, and trajectory Open 02 Offline eval sets: curation, hard negatives, contamination, and versioning Open 03 LLM-as-judge and human evaluation: calibration, bias, variance, and agreement Open 04 Evaluating agent and tool trajectories: success, efficiency, recovery, and policy compliance Open 05 Online evaluation: shadow, canary, A/B, guardrails, and regression gates Open 06 Observability, failure taxonomies, and production → eval → repair feedback loops Open No content matches this search.
For a while it was reasonable to describe multimodality as the story of a language model learning to look at images, but that account is now too narrow: the field includes models that combine text, images, video, audio and documents inside a shared representational system.
Gemini was introduced from the beginning as a multimodal family spanning text, audio, image, video and code. Gemini Embedding 2 also turns multimodal embedding into a native primitive across text, images, video, audio and documents. Qwen2.5-Omni pushes streaming multimodal input and output, while PaLM-E reminds us that once robotics or environmental state enters the system, the boundary of the problem moves again.
This series therefore treats multimodality not as an appendix to LLMs or a catalogue of image-text pairs, but as a broader problem: how to preserve evidence from different modalities, align signals when they refer to the same object or event, reason over them without destroying important information, and in some cases produce multimodal outputs or act through tools and the environment.
Complete
General
~45 min
5 chapters
Contents
1. The real problem: what counts as multimodality
What a modality is and why text, image, audio, video, documents and sensors behave differently.
Why "turn everything into text" solves some tasks while throwing away part of the problem.
What changes when systems move from text-centric designs to multiple input and output modalities.
2. Alignment: from pairs to interactions
How a system learns that two different signals refer to the same object, event or context.
What changes when alignment is not only image-text, but audio-text, video-audio, document-layout or perception-action.
Why data structure and data quality matter more than model rhetoric.
3. Architectures: shared spaces, connectors and omni models
Dual encoders, cross-attention, lightweight connectors, interleaved sequences and more unified models.
The tradeoffs of each family in cost, latency, flexibility and grounding.
Why multimodal embedding and multimodal generation are not the same layer of the system.
4. Evaluation
Why evaluating multimodality requires more than accuracy on image question answering.
Grounding, localization, time, documents, audio and heterogeneous output formats.
What recent benchmarks reveal about the field's actual limits.
5. Risks
Multimodal prompt injection, privacy in documents and images, voice security and tool use.
What changes when the system does not only answer, but acts.
Why multimodality expands the attack surface and the error surface at the same time.
Related series: AI and Generative AI Foundations · Reasoning Models
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Learning path
Continue from here Understand the concept Evaluating AI models How to evaluate an AI model and system with benchmarks, reference sets, judges, human review and product metrics without confusing a score with real value. Read next The real problem of multimodality What it means to integrate text, images, audio and other modalities, and how perception, alignment, reasoning, generation and action fit together. Watch next Structure, resolution and time If that position answered the question, the reduction already removed the needed evidence. Try it Agent reliability and evaluation — trace playground Evaluate final success, first-pass success, retries, tool decisions, timeouts, policy adherence and trajectory efficiency with explicit release gates.