Multimodal AI roadmap

Building with models that see, hear and read documents. The application patterns are familiar; what changes is that inputs are large, expensive, and fail in ways text never does.

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Application-focused: using multimodal models, not training them. Section 3 (document understanding) is the highest-value part for most teams, because it is where multimodal quietly fixes the problem that limits half of all RAG systems. Sections 5 and 6 are worth reading even if you never build with audio or video, because the cost and latency shapes are instructive.

1

Foundations

          2

          Vision Basics

                  3

                  Document Understanding

                  The highest-value application for most teams. Parsing quality sets the ceiling for every downstream RAG system, and this is where it gets fixed.

                              4

                              Multimodal Retrieval

                                      5

                                      Audio

                                              6

                                              Video

                                                      7

                                                      Generation

                                                              8

                                                              Engineering

                                                                          9

                                                                          Evaluation

                                                                                  Choosing an approach for documents

                                                                                  The most common multimodal decision, and the one with the largest quality consequences.

                                                                                  1. Try text extraction first. If the PDF has a clean text layer and simple layout, a parser is faster, cheaper and more deterministic than a vision model.
                                                                                  2. Check what the parser dropped. Render a page and compare it against the extracted text. Tables, multi-column flow and figure captions are where extraction usually fails silently.
                                                                                  3. Use vision for the pages that fail. A hybrid pipeline - text extraction by default, vision for problem pages - beats picking one approach for everything.
                                                                                  4. Consider page-image retrieval when layout carries meaning: forms, invoices, scientific papers, slide decks.
                                                                                  5. Budget the tokens. A page image can cost more than a page of text by a wide margin. At corpus scale this decides the architecture.
                                                                                  6. Evaluate extraction directly. Not answer quality at the end - parse quality at the start. Most teams never measure this and blame the model for what the parser lost.

                                                                                  Step six is the one that pays. Ingestion quality sets the ceiling on everything downstream.

                                                                                  A vision model reading a document is not doing OCR. It is interpreting, and it will smooth over an ambiguous digit rather than flag it. That is an improvement for comprehension and a hazard for anything where the exact value matters.