RAG models in low-code applications

AI in Business

Today, AI models can be found everywhere, from AI-assisted human-machine interaction, content generation and analysis, images, sounds, research, behavioral profiling and fraud detection, virtual assistants and recruiters, medicine, diagnostics, to noise reduction in photography.

Challenges of Large Language Models (LLM)

The use of large language models (LLM) in critical applications for interpretation and intelligent processing of data has made the implementation of artificial intelligence a priority in the corporate market as well. LLM models are trained on huge datasets, but their knowledge is independent of the field and limited to the training stage. In order to take into account new or specific data, re-training is necessary.

RAG — a way to better AI results

Interactions between LLM-supported applications and humans must be monitored to limit hallucinations and errors. The solution to this challenge is RAG — Retrieval Augmented Generation (generation extended with semantic search). RAG allows you to:

  • include additional data (e.g. company) in the prompts,
  • guide the model to generate contextually relevant responses,
  • reduce hallucinations and data errors.

Thanks to this, the user receives more precise results, enriched with semantically relevant knowledge.

RAG models on the Meltemee low-code platform

Application of RAG models in our low-code platform Meltemee improves the efficiency of the work of both the solution designer and the end user.

Key benefits:
  • Improved readability of messages — RAG processes LLM responses to make them more understandable to the user.
  • New low-code capabilities — semantic search and analysis of documents support sales processes or the creation of knowledge bases.
  • Process automation — RAG enables automation of previously unsupported tasks, such as:
    - handling of correspondence and complaints,
    - processing of applications and applications,
    - employee onboarding,
    - certification and other internal and external processes.