Currently, many businesses are deploying some form of RAG. The promise is attractive: index your PDFs, connect an LLM and immediately democratize your corporate knowledge.

But for industries that rely on heavy engineering, the reality is grim. The engineers ask specific questions about the infrastructure, and the bot hallucinates.

The failure not in LLM. The failure lies in the preprocessing.

Standard RAG pipelines treat documents as flat strings of text. They use "fixed-size chunking" (truncating one document every 500 characters). This works for prose, but it breaks the logic of technical manuals. It cuts tables in half, separates captions from images, and ignores the page’s visual hierarchy.

Iimproving RAG reliability is not about buying a bigger model; it’s about fixing the "dark data" problem through semantic chunking and multimodal textualization.

Here is the architectural framework for building a RAG system that can actually read a manual.

The fallacy of fixed-size chunking

In a standard Python RAG tutorial, you split text by character count. In a business PDF, this is disastrous.

If a security specification table spans 1,000 tokens, and your chunk size is 500, you just split the "voltage limit" header from "240V" value. the vector database it is stored separately. If a user asks, "What is the voltage limit?"the retrieval system will look for the header but not the value. LLM, forced to answer, is always guessing.

The solution: Semantic chunking

The first step in improving production RAG is to abandon arbitrary character numbers in favor of document intelligence.

Using a layout-aware parsing tool (such as Azure Document Intelligence), we can segment data based on document structure such as chapters, sections and paragraphs, instead of token count.

• Logical unity: A section that describes a specific part of the engine is stored as a vector, even if it varies in length.

• Preserving the table: The parser introduces a table boundary and compresses the entire grid into a fragment, preserving the row-column relationships that are important for accurate retrieval.

In our internal qualitative benchmarks, the transition from fixed to semantic chunking significantly improved the retrieval accuracy of tabular data, effectively stopping the fragmentation of technical specs.

Unlocking visual dark data

The second method of RAG business failure is blindness. A large amount of corporate IP is not in text, but in flowcharts, schematics and system architecture diagrams. Standard embedding models (such as text-embedding-3-small) are not enabled "See" these pictures. They are skipped during indexing.

If your answer is in a flowchart, your RAG system will say, "I don’t know."

The solution: Multimodal textualization

To search for diagrams, we implement a multimodal preprocessing step using vision-capable models (specifically GPT-4o) before the data hits the vector store.

1. Getting OCR: High-precision optical character recognition pulls text labels from within the image.

2. Generative captioning: The vision model analyzes the image and creates a detailed description in natural language ("A flowchart showing that process A leads to process B when the temperature exceeds 50 degrees").

3. Hybrid embedding: This generated description is entered and stored as metadata associated with the original image.

Now, when a user searches "process flow temperature," finding the vector corresponds to ILLUSTRATIONalthough the original source is a PNG file.

The trust layer: evidence-based UI

For business adoption, accuracy is only half the battle. The other half is verification.

In a standard RAG interface, the chatbot provides a text response and mentions a filename. It forces the user to download the PDF and search the page to verify the claim. For long questions ("Is this chemical flammable?"), users will never trust the bot.

the architecture should be implemented visual citation. Because we preserve the link between the text chunk and its parent image during the preprocessing stage, the UI can display the exact chart or table used to generate the answer alongside the text answer.

it "show your work" The mechanism allows humans to instantly verify AI reasoning, bridging the trust gap that has killed so many. internal AI projects.

Future proofing: Native multimodal embeddings

While the "textualization" method (converting images into text descriptions) is the practical solution for today, the architecture is developing rapidly.

We have seen the emergence of native multimodal embeddings (like Cohere’s Embed 4). These models can map text and images to the same vector space without the middle step of captioning. While we currently use a multi-stage pipeline for maximum control, the future of data infrastructure is likely to be "end-to-end" vectorization where the layout of a page is directly embedded.

Moreover, as high context LLMs be cost-effective, the need for chunking can be reduced. We can pass entire manuals to the context window. However, until the latency and cost for millions of token calls drops significantly, semantic preprocessing remains the most economical method for real-time systems.

CONCLUSION

The difference between a RAG demo and a production system is how it handles the messy reality of business data.

Stop treating your documents as simple strings of text. If you want your AI to understand your business, you need to respect the structure of your documents. By implementing semantic chunking and unlocking visual data within your charts, you transform your RAG system from a "keyword searcher" in a true "helper of knowledge."

Dippu Kumar Singh is an AI architect and data engineer.

Source link