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How to “train” a neural network on your data: a step-by-step guide

🕑 3 min · LearnAI

Standard AI models have a wealth of knowledge, but often lack the specificity needed for your unique challenges or enterprise information. Imagine AI being able to work with your internal documents, knowledge base, or even your personal style. This is not only possible, but has become available to most users. We will tell you how to step-by-step “tune” a neural network to your data, significantly increasing its value.

Define the purpose and scope of the data

Before you begin, be clear about what problem the “trained” model needs to solve. Do you need to generate texts in a specific style? Answer questions about your products? The more specific the goal, the easier it is to select the necessary data. Gather all relevant documents, texts, examples - anything that reflects your information or desired style. The quality and relevance of your data directly affects the final result.

Prepare data for download

AI learns information better when it is well structured. Clear texts of excess “garbage”, remove duplicates, format data in a form convenient for the model (for example, JSON, CSV or simple text files). Break large documents into smaller, logical parts. This step is critical: poorly prepared data will lead to inaccurate or incoherent answers, frustrating your efforts.

Select data integration method

There are several ways to introduce AI to your data. The simplest one is to insert data directly into the prompt (contextual learning). For larger volumes, use retrieval and generation (RAG) methods, where the model first searches your database for information and then generates a response. For deep customization, consider fine-tuning - this requires more resources, but gives the best results for very specific tasks.

Test and adjust

After data integration, be sure to check how well the model copes with new tasks. Ask her questions that require access to your data. Evaluate the accuracy, relevance, and completeness of responses. If there are any shortcomings, analyze at what stage the problem arose: in the preparation of data or in the formulation of the request. Repeat the process until you reach the desired quality.

Update data and stay current

Business processes change, information becomes outdated. To keep AI useful, regularly update the database it runs on. Add new documents and update old ones. It's a continuous process. This approach ensures that your AI assistant will always operate with the latest and most accurate information, becoming a truly indispensable tool in your work.

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