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Multimodal Answer Synthesizer

Software componentKnowledge & DataKnowledge & Dataarc:MultimodalAnswerSynthesizer

An answer synthesizer that prompts a vision-language model with the query, retrieved text, and retrieved images to produce grounded answers, including visual question answering.

Responsibility. Generates answers grounded in both retrieved text and original visual evidence.

Also known as: Visual question answering (VQA), Multimodal LLM answering

Variant of Answer Synthesizer abstract

Direct neighbourhood (hover for relationship types)

Relationships

invokes dependency

OpenAI-Compatible Inference API Ch2.7

receives data from dynamic

Multimodal Context Assembler Ch2.7

Design guidance

SHOULD use temperature 0.1 for visual question answering to keep answers grounded in visual evidence.
SHOULD ask specific, constrained questions of the vision-language model rather than open-ended ones.
SHOULD invoke the multimodal model only for the small number of retrieved items to limit cost.

Quantitative guidance

As stated by the sources; verify before use.

VQA temperature 0.1 (Ch2.7).

Classification

Patterns: Visual question answering
Technologies: NVIDIA NeVA 22BGPT-4V
Quality attributes: Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)
Risks mitigated: Hallucinated visual details

Sources

Ch2.7: T. Nguyen, "Multimodal RAG Approaches," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.7. ISBN: 9798244538229.