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Solutions / Choosing and Deploying an LLM or NLP Model Within the Company Infrastructure

The implementation of natural language processing (LLM) models is becoming a crucial part of business automation and digital transformation. However, selecting a specific model requires a careful analysis of a company’s needs, technical capabilities, and business goals. In this article, we explore the main tasks that an LLM can solve, selection criteria, popular models, and alternative approaches.

 

 

1. What tasks can an LLM solve?

Large language models (LLMs) are used across various business domains. Common use cases include:

  • Customer support automation: chatbots and virtual assistants that handle user inquiries.
  • Content generation: creating marketing materials, reports, and product descriptions.
  • Text analysis: processing feedback, resumes, and documents.
  • Translation and localization: automatic translation into different languages.
  • Information retrieval: intelligent search engines that answer user questions.
  • Data classification: sentiment analysis, categorizing user requests.

 

 

2. Why is choosing an LLM challenging?

Selecting an LLM depends on multiple factors, including:

  • Task requirements: some models are better for text generation, others for analysis.
  • Computational resources: models vary greatly in memory and processing needs.
  • Data confidentiality: cloud-based models may not be suitable for security-sensitive organizations.
  • Cost: large models can be expensive to train and maintain.

 

 

3. Popular LLM models: pros and cons

Some of the most well-known LLMs include:

  • GPT-4 (OpenAI)
    Pros: high accuracy, flexible text generation, wide range of tasks.
    Cons: requires substantial computing power, high cost.
  • LLaMA (Meta)
    Pros: deployable on-premises, optimized architecture.
    Cons: complex setup, less mature API.
  • Claude (Anthropic)
    Pros: improved safety, handles long contexts.
    Cons: limited access, potentially high costs.
  • Mistral
    Pros: high-performance, suitable for open-source deployments.
    Cons: trained on a smaller dataset than GPT.

 

 

4. When is an NLP model (like BERT) enough?

Not all tasks require a full LLM. NLP models like BERT (Bidirectional Encoder Representations from Transformers) are often sufficient.

Advantages of BERT:
  • Great for classification, search, sentiment analysis tasks.
  • Requires fewer computing resources.
  • Available in open-source versions (BERT, RoBERTa, DistilBERT).
Disadvantages of BERT:
  • Not suitable for text generation tasks.
  • Limited support for long-context understanding.

 

 

5. What to choose: LLM or BERT?

If your goal is text generation, chatbot development, or handling long contexts — choose an LLM.

If the task involves text analysis (sentiment, classification, data extraction) — BERT or another NLP model is sufficient.

 

 

Demo assistant

You can try a generative AI assistant in Telegram or WhatsApp. The bot is trained to answer frequently asked questions for a furniture company "EuroMebel".

 

 

Conclusion

The right model depends on your business task, budget, and available resources. LLMs are suitable for complex and multifunctional use cases, while BERT and other NLP models are often ideal for focused analytical tasks. Before making a final decision, it’s worth testing different approaches to find the best balance between quality and cost.