The next stage in the evolution of LLM application in business is the integration of models with actual real-time corporate data. This approach is called RAG (Retrieval-Augmented Generation) – Retrieval-Augmented Generation. In this architecture, the language model becomes not just a dialog interface, but a full-fledged intelligent assistant capable of navigating documents, drawings, databases and providing accurate, contextualized answers.
The main advantage of RAG is the ability to utilize internal company data without the need to pre-train the model, while maintaining high accuracy and flexibility in information handling.
RAG technology combines two main components:
- Retrieval: the model connects to data stores – documents, tables, PDF -files, drawings – and retrieves relevant information as requested by the user.
- Augmented Generation: based on the extracted data, the model generates an accurate, informed response, taking into account the context and specificity of the query.
In order to run LLM with RAG support, there are a few steps to follow:
- Data preparation: gather the necessary documents, drawings, specifications, tables. They can be in different formats and structures, from PDF to Excel.
- Indexing and vectorization: using tools such as LlamaIndex or LangChain, data is converted into vector representations that allow you to find semantic links between text fragments (more about vector databases and translating large arrays into vector representation, including CAD projects, in Part 8).
- Query the assistant: once the data has been uploaded, you can ask the model questions and it will search for answers within the corporate framework rather than in general knowledge gathered from the internet.
Suppose a company has a folder constructionsite_docs, where contracts, instructions, estimates and tables are stored. Using a Python script (Fig. 3.3-5), we can scan this folder and build vector indexing: each document will be converted into a set of vectors reflecting the semantic content of the text. This turns the documents into a kind of “map of meanings” on which the model can efficiently navigate and find connections between terms and phrases.
For example, the model “remembers” that the words “return” and “complaint” are often found in the section of the contract concerning the shipment of materials to the construction site. Then, if a question is asked – for example, “What is our return period?” (Fig. 3.3-5 – line 11 of code) – the LLM will analyze internal documents and find accurate information, acting like an intelligent assistant capable of reading and understanding the contents of all corporate files.
The code can be run on any computer with Python installed. We’ll talk more about using Python and IDEs to run the code in the next chapter.
Local deployment of LLM is not just a trend, but a strategic solution for companies that value security and flexibility. However, deploying LLM, whether on local company computers or using online solutions, is only the first step. In order to apply LLM capabilities to real-world tasks, companies must utilize tools that allow them to not only receive chat responses, but also store the logic created in the form of code that can be run outside of the context of using LLM. This is important for scaling solutions – properly organized processes make it possible to apply AI developments to several projects or even the entire company at once.
In this context, the choice of a suitable development environment (IDE) plays an important role. Modern programming tools allow not only to develop LLM-based solutions, but also to integrate them into existing business processes, turning them into automated ETL -Pipeline
