The Problem With Vision Language Models

Key Concepts

• Vision Language Models (VLMs): AI models like Gemini, GPT-5, and Mistral that process both image and text data.
• Generative vs. Non-Generative AI: Generative AI creates new content, while non-generative AI extracts existing content.
• Hallucinations (in AI): Instances where an AI model generates incorrect or fabricated information.
• Verbatim Data Extraction: Extracting data exactly as it appears in the original source, without alteration.
• Semantic Structure: The meaning and relationships between elements within a document (e.g., headers, tables, paragraphs).
• Dockling: An open-source tool for document text extraction using non-generative AI models.
• RAG (Retrieval-Augmented Generation): A technique for improving the accuracy of generative AI by grounding it in retrieved data.

The Limitations of Vision Language Models for Document Extraction

The core argument presented is that while Vision Language Models (VLMs) – specifically mentioning Gemini, GPT-5, and Mistral – demonstrate impressive capabilities, their inherent generative nature makes them unsuitable for applications requiring precise, verbatim document extraction. VLMs don’t actually extract text; they predict text based on visual input. This predictive process, while effective for deciphering messy scans or handwriting, introduces the risk of “hallucinations” – the generation of information not present in the original document.

The speaker clarifies that hallucinations aren’t necessarily problematic for all use cases. However, when absolute accuracy and a faithful reproduction of the source material are critical, the predictive nature of VLMs becomes a significant drawback. The need for “verbatim scans” and “verbatim data extraction” necessitates a different approach.

Dockling: A Non-Generative Alternative

Dockling is presented as a solution to this problem. It’s described as a tool utilizing a “standard pipeline” of non-generative AI models specifically designed for text extraction. These models are purpose-built to recognize document elements like “headers” and “table layouts” while crucially “preserving the semantic structure of the document.” This means Dockling focuses on accurately identifying and extracting the existing information, rather than creating new content.

The speaker emphasizes that Dockling’s pipeline isn’t necessarily “smarter” than VLMs; it’s simply a “different tool for the job.” It’s optimized for extraction accuracy, whereas VLMs excel at generation and interpretation.

Hybrid Approach & Local Execution

The transcript highlights the flexibility of Dockling by stating that VLMs can also be run through Dockling. This suggests a potential hybrid approach where Dockling handles the initial, precise extraction, and a VLM could then be used for further analysis or summarization.

A key advantage of Dockling is its “completely open source” nature and its ability to run “locally.” This is presented as a significant benefit, particularly for building “fully grounded AI agents” – AI systems whose responses are directly tied to verifiable data sources. The speaker references a linked tutorial demonstrating the construction and deployment of such an agent.

RAG and Grounded AI Agents

The mention of a “fully local AI rag agent” introduces the concept of Retrieval-Augmented Generation (RAG). RAG is a technique where a generative AI model’s output is informed by retrieving relevant information from a knowledge base. Running Dockling locally and integrating it with a RAG pipeline ensures that the AI agent’s responses are “fully grounded” – meaning they are based on verifiable data extracted by Dockling, minimizing the risk of hallucinations.

Conclusion

The primary takeaway is a critical distinction between generative and non-generative AI approaches to document extraction. While VLMs offer powerful capabilities, their inherent predictive nature can compromise accuracy when verbatim data is required. Dockling provides a robust, open-source alternative focused on precise extraction and semantic preservation, particularly valuable for building reliable and grounded AI agents. The flexibility to integrate VLMs within the Dockling framework allows for a hybrid approach leveraging the strengths of both technologies.