Ertaoza models · LLM
Georgian LLM: the language layer of a conversation
Ertaoza has its own LLM for Georgian-language work. A large language model processes text and context, helping prepare an answer or the next conversational step. In a voice system, it sits between recognized speech and the spoken response.
From a question to a bounded answer
Question and context
Provide the request and the approved reference information.
Language processing
Prepare a response within the defined task.
Check or hand over
Review the result and handle missing information safely.
What is a Georgian LLM?
A large language model, or LLM, is an AI model used to process and generate language. A Georgian-language application must account for Georgian wording and context. Its output still needs boundaries: fluent text is not proof that a fact is correct or that an external action has happened.
Georgian wording and the meaning of a request
The same request can be expressed with different word order, inflected forms or borrowed terms. A dialogue project should include those variations in its evaluation rather than relying on one ideal prompt.
Define whether the task is answering, explaining, summarizing, classifying or preparing structured information. Clear task boundaries help distinguish a useful answer from text that merely sounds plausible.
Use approved knowledge instead of guessed business facts
Service rules, prices, schedules and customer records should come from a designated source. A language model is not automatically connected to current business information. If the necessary source is missing, the system should say so or ask for help.
Retrieval-augmented generation, often called RAG, is an approach for supplying relevant reference material to a model. Whether a project needs retrieval, a direct system query or a simpler rule-based answer depends on the task. This page does not claim an already configured knowledge integration.
How the LLM works with STT and TTS
STT supplies the recognized request as text. The language layer uses that text and the available context to prepare a response. TTS can then make the response audible. Each component can introduce a different kind of error.
For actions such as booking, keep authorization and execution in the application workflow. The model can help interpret the request, but it should not decide its own permissions or turn an unverified outcome into a success message.
Review answers against a defined task
Evaluate factual consistency, Georgian readability, use of the approved source and behavior when information is missing. Include ambiguous questions and instructions that attempt to bypass the intended task. A human reviewer should be able to identify why an answer is acceptable.
- Does the answer address the actual question?
- Can factual claims be checked against the approved source?
- Does it acknowledge missing information rather than inventing it?
- Does it respect the boundary between answering and acting?
Model ownership and technical specifications are different
Ertaoza has its own LLM. That statement does not establish a parameter count, training-data size, architecture, open-source licence or training-from-scratch method. Version-specific documentation and project evaluation are needed before making those comparisons or choosing an access model.
Questions and answers
Does Ertaoza have its own Georgian LLM?
Yes. Ertaoza has its own language model for Georgian-language work. This page explains the role of that model without inventing a version number or technical specifications.
Is an LLM the same as speech recognition?
No. STT recognizes speech and produces text. The LLM works with language and context. TTS performs the separate task of turning response text into speech.
Can an LLM answer from my company’s documents?
That requires a defined knowledge workflow, document access and an evaluation of the answers. Discuss which documents are approved, how updates are handled and what the system should do when no answer is supported.
Are all generated answers accurate?
No. Language models can generate incorrect or unsupported statements. Use approved sources, explicit limits and human review where the outcome matters.
Scope and availability
No model size, benchmark, training method, licence or public API is specified on this page. Those details should not be inferred from the term “own model.”
Ertaoza · Updated
Continue with the right next step
Tell us about the task
Describe the language, channel and result you need. Start with non-sensitive examples; agree on access and data handling before sharing recordings or customer information.
Discuss a Georgian language-model task