Multi-Modal Context Injection

Expert

Structure prompts to reference images, video, and audio contexts.

01 The Concept

Multi-modal models accept video, audio, and images. Engineering multi-modal prompts requires referencing specific timestamps (e.g. 'At 0:15 in the video...') and bounding text contexts (like transcriptions) alongside the media file to align reasoning across modalities.

02 Weak vs. Strong

EX 01Multi-Modal Video Audit Prompt

You are a traffic audit assistant. Analyze the attached video alongside the GPS metadata log below. GPS Metadata: - 0:00 - 0:10: Speed 45mph - 0:10 - 0:20: Speed 0mph Task: Identify the cause of the stop. Focus your visual analysis on the upper-right quadrant of the frame between timestamps 0:10 and 0:15.

→ Why it works

Aligns GPS telemetry with video frames and directs visual attention to the exact quadrant and time window, improving classification accuracy.

03 Key Points

01Temporal Gating: Referencing specific timestamps or frame indices in instructions.

02Spatial Anchoring: Directing attention to specific image coordinates or sectors.

03Modal Alignment: Matching audio transcripts with corresponding video timelines.

04Feature Bounding: Wrapping text context (metadata) in tags next to media inputs.

05Context Layout: Order media files and metadata prompts systematically to optimize parsing.

04 Model-Specific Notes

Claude 3.5 Sonnet supports high-resolution image inputs. Use clear spatial coordinate terms (e.g. 'lower-left quadrant') in prompts.

05 For Your Role

It's like telling someone: 'Look at the photo I sent, specifically the dog sitting under the table on the left.'

Next: Prompt Ensembling via MoE →