By now, many enterprises have deployed some form of RAG. The promise is enticing: index your PDFs, connect LLM and instantly democratize your corporate knowledge.
But for industries dependent on heavy engineering, the reality has been disappointing. Engineers ask specific questions about the infrastructure, and the bot hallucinates.
Failure is not in LLM. The failure is in preprocessing.
Standard RAG pipelines treat documents as flat strings of text. they use "fixed size segmentation" (Cutting a document every 500 characters). This works for prose, but it destroys the logic of the technical manual. It cuts tables in half, separates captions from images, and ignores the visual hierarchy of the page.
IImproving RAG reliability isn’t about buying a larger model; it’s about healing "dark data" Problem solving through semantic chunking and multimodal textualization.
Here is the architectural framework for building a RAG system that can actually read a manual.
Fallacy of fixed-sized pieces
In a standard Python RAG tutorial, you split text based on character count. In enterprise PDF, this is disastrous.
If a security specification table spans 1,000 tokens, and your shard size is 500, you have just divided "voltage range" header from "240V" price. The vector database stores them separately. When a user asks, "What is the voltage limit?"The recovery system finds the header but not the value. Forced to answer, LLMs often guess.
Solution: Semantic Chunking
The first step to fixing production RAG is to abandon arbitrary character counting in favor of document intelligence.
Using layout-aware parsing tools (such as Azure Document Intelligence), we can split data based on document structure such as chapters, sections, and paragraphs, rather than token count.
- Logical Coherence: The section describing a specific machine part is kept as a single vector, even if it has different lengths.
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Table Protection: The parser identifies a table boundary and binds the entire grid into a single chunk, preserving row-column relationships that are critical for accurate retrieval.
In our internal qualitative benchmarks, moving from fixed to semantic chunking significantly improved the retrieval accuracy of tabular data, effectively preventing fragmentation of technical specifications.
Unlocking visual dark data
The second failure mode of enterprise RAG is blindness. A large amount of corporate IP exists not in text, but in flowcharts, schematics, and system architecture diagrams. Standard embedding models (e.g. text-embedding-3-small) cannot "Look" These images. They are discarded during indexing.
If your answer is in the flowchart, your RAG system will say, "I don’t know."
Solution: Multimodal Textualization
To make the diagrams searchable, we applied a multimodel preprocessing step using vision-enabled models (specifically GPT-4o) before the data accessed the vector store.
- OCR extraction: High precision optical character recognition draws text labels from within the image.
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Generative Captioning: The vision model analyzes the image and generates a detailed natural language description ("A flowchart showing how process A leads to process B if the temperature exceeds 50 degrees").
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Hybrid Embedding: This generated description is embedded and stored as metadata associated with the original image.
Now, when a user searches "temperature process flow," vector search matches DescriptionEven if the original source was a PNG file.
Trust Layer: Evidence-Based UI
For enterprise adoption, accuracy is only half the battle. the second part is verifiability.
In a standard RAG interface, the chatbot returns a text reply and suggests a file name. This forces the user to download the PDF and look for the page to verify the claim. For high stakes questions ("Is this chemical flammable?"), users will not trust the bot at all.
Architecture should implement visual citation. Because we preserved the link between the text fragment and its original image during the preprocessing step, the UI can display the exact chart or table used to generate the answer along with the text response.
it "show your work" The mechanism allows humans to instantly verify an AI’s reasoning, bridging the trust gap that plagues many internal AI projects.
Future-Proofing: Basic Multimodal Embedding
When "textualization" The method (converting images to text description) is a practical solution for today, the architecture is rapidly evolving.
We are already seeing the emergence of native multimodal embedding (Such as Cohere Embed 4). These models can map text and images into the same vector space without the intermediate step of captioning. While we currently use multi-stage pipelines for maximum control, future data infrastructure is likely to include "from beginning to end" Variant where the layout of a page is directly embedded.
Moreover, like long reference llm Once cost effective, the need for chunking may be reduced. We can soon pass the entire manual into the reference window. However, until latency and cost for million-token calls are significantly reduced, semantic preprocessing remains the most economically viable strategy for real-time systems.
conclusion
The difference between the RAG demo and the production system is how it handles the messy reality of enterprise data.
Stop treating your documents as simple lines of text. If you want your AI to understand your business, you must respect the structure of your documents. By implementing semantic chunking and unlocking the visual data within your charts, you transform your RAG system from one "keyword searcher" one really "Knowledge Assistant."
Dippu Kumar Singh is an AI architect and data engineer.
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