Systems, agents, retrieval, infrastructure

Advanced Multimodal RAG Optimization

Retrieval-augmented generation has moved beyond text chunks and vector search. Modern systems need to retrieve from PDFs, diagrams, tables, screenshots, transcripts, product analytics, video frames, and image-heavy documentation while preserving provenance and layout-sensitive meaning.

Deep dive summary

What this module actually teaches.

Passage 1Retrieval-augmented generation has moved beyond text chunks and vector search. Modern systems need to retrieve from PDFs, diagrams, tables, screenshots, transcripts, product analytics, video frames, and image-heavy documentation while preserving provenance and layout-sensitive meaning.

Passage 2This module explores multimodal indexing strategies: document layout parsing, table extraction, image captioning, late-interaction retrieval, hybrid lexical-vector search, rerankers, query decomposition, citation packing, and context compression. Learners compare when to embed raw text, generated captions, region crops, page images, or graph relationships.

Passage 3The optimization focus is evaluation-driven. Students design retrieval test sets, measure recall and groundedness, inspect failure cases, and tune chunking, reranking, and context assembly so generated answers cite evidence rather than merely sound plausible.

Learning operating system

Module prerequisites, concepts, outcomes, and artifacts

Prerequisites

  • RAG fundamentals
  • embedding search concepts
  • basic evaluation methodology

Key concepts

  • multimodal embeddings
  • hybrid retrieval
  • late-interaction reranking
  • layout-aware parsing
  • citation packing
  • context compression
  • groundedness evaluation

Target audience

  • Engineers building knowledge assistants, document intelligence products, research tooling, or enterprise search layers.

Outcomes

  • Design a RAG pipeline for text, table, image, and slide content
  • Evaluate retrieval quality before judging model answer quality
  • Reduce hallucinations through evidence-aware context assembly

Artifacts

  • multimodal RAG architecture map
  • retrieval evaluation rubric
  • read-only citation packing example

Code example

Real patterns, ready to copy into your editor.

A code reference card that frames this module's concepts in real syntax. Copy any example to study it or adapt it directly in your own editor.

Copy-ready snippets
curriculum/se-advanced-multimodal-rag-optimization.tsts
1type ModuleBrief = {2  track: string;3  difficulty: string;4  keyConcepts: string[];5  reviewGate: 'human-review-required';6};7 8export const moduleBrief: ModuleBrief = {9  track: 'Software Engineers',10  difficulty: 'Masterclass',11  keyConcepts: [12    'multimodal embeddings',13    'hybrid retrieval',14    'late-interaction reranking',15    'layout-aware parsing',16    'citation packing',17  ],18  reviewGate: 'human-review-required',19};