Markdown source

Slides: Bypassing the Multimodal Tax: Hybrid RAG, SQL RRF & UI Telemetry - Abed Matini, Ogilvy

Source Video

Bypassing the Multimodal Tax: Hybrid RAG, SQL RRF & UI Telemetry - Abed Matini, Ogilvy

Relationship To World's Fair 2026

These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

Related Scheduled Sessions

• No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

Extracted Slides

• Recreated text/layout view: open HTML recreation
• AI slide classifier: title_card confidence 0.99
• Text source: agent_vision.

Slide text:

Bypassing the Multimodal Tax

Framework-Free Hybrid RAG, Raw SQL RRF, and Live UI Telemetry

Abed Matini

Senior Backend Developer - Ogilvy

AI Engineer World's Fair 2026 - Online Track

• Recreated text/layout view: open HTML recreation
• AI slide classifier: content_slide confidence 0.97
• Text source: agent_vision.
• OCR decision: ready — Dense small text in two-column slide content; OCR will be cheaper and more reliable than direct transcription.

Slide text:

Two problems every document chatbot hits

• Recreated text/layout view: open HTML recreation
• AI slide classifier: content_slide confidence 0.93
• Text source: none.
• OCR decision: ready — Browser/document screenshot with dense small text and table-of-contents content; OCR is the right next step.

• Recreated text/layout view: open HTML recreation
• AI slide classifier: content_slide confidence 0.96
• Text source: agent_vision.
• OCR decision: ready — Product/admin UI screenshot with dense table text and controls; OCR is appropriate.

Slide text:

Upload document

• Recreated text/layout view: open HTML recreation
• AI slide classifier: content_slide confidence 0.95
• Text source: none.
• OCR decision: ready — Tabbed document inspector with dense table-of-contents text; OCR is the right pass.

• Recreated text/layout view: open HTML recreation
• AI slide classifier: content_slide confidence 0.97
• Text source: agent_vision.

Slide text:

Thank you

Questions welcome on LinkedIn.

Hidden Non-Slide Evidence

• `slide-006.jpg` — demo_video confidence 0.99; File picker overlay from demo footage, not a readable presentation slide.
• `slide-007.jpg` — demo_video confidence 0.99; Trace-detail app screenshot from demo footage, not a presentation slide.
• `slide-008.jpg` — demo_video confidence 0.99; Trace-detail app screenshot from demo footage, not a presentation slide.

Classification audit: raw/sources/slide-ai-classification/slides/Akm1sqvWG4A/audit.json

Slide-Derived Subjects To Review

Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.