AI consulting

LLM-as-a-judge (LLM judge)

What is a judge LLM, and why is it important?

A judge LLM is a special AI subsystem that does not generate answers, but evaluates answers generated by another LLM: for example, it decides whether they are correct, understandable, safe, and whether they comply with specific corporate or external regulations.

What do we offer?

A unique evaluation framework

It maps expert expectations, so that the AI assistant’s responses are evaluated based on business-critical criteria

Its central element is an evaluation taxonomy tailored to the field

It is based on real conversations and can only be applied where relevant

LLM-as-a-judge (or judge LLM)

An AI-based evaluation subsystem that reviews the AI assistant’s responses and filters out potential problems before they reach the user

Safety mechanism

If LLM-as-a-judge is not sufficiently confident in its evaluation, the case is referred to a human expert, whose feedback improves the system

Optional integration

Into the MLOps or product development process so that evaluation can be automated and integrated

Who is it for?

AI product teams, organisational units engaged in AI-based innovation, and their leaders at medium and large companies and institutions that are implementing or would like to implement conversation-based AI solutions (AI assistants) – especially in regulated, critical or domain-specific environments (e.g. banking, logistics, healthcare, government institutions).

Companies involved in AI-based development.

What business problems do we solve?

Hallucinations

Large language models can provide answers that seem convincing but are actually incorrect or misleading.

Compliance

Ensuring compliance with rules, guidelines, and regulations in the responses of AI assistants is challenging.

Inconsistent evaluation

It is often difficult to judge objectively and based on a uniform set of criteria whether an AI assistant is actually providing good answers, especially when a large amount of generated content needs to be checked in a short period of time, even in real time.

Slow human verification

Manual evaluation is costly and cannot be scaled to rapid development cycles.

Ineffective feedback mechanisms

Developers often do not receive sufficiently detailed feedback on what constitutes a “good answer” from a business perspective, or are unable to translate it appropriately into the language of technology.

Why choose us?

We combine machine learning expertise

We combine machine learning expertise with business process knowledge, building a bridge between developers and subject matter experts.

With our structured approach

Abstract expectations become measurable, testable logic.

With our lean, MVP-based methodology

The project can be started or continued in small steps with immediate results, and the results can be scaled quickly and confidently.

How does it work?

Conversation data analysis

We identify relevant evaluation criteria using real AI assistant-user interactions and involving domain experts.

Taxonomy building

A kritériumokat szakterület, téma és beszélgetéstípus szerint strukturáljuk, egy folyamatosan fejlődő információs bázist létrehozva

Prompt development

We create targeted evaluation components for each criterion that can be injected into prompts, compete them, and select the most effective ones based on objective tests.

LLM-as-a-judge alignment

Based on the results of the previous points, we fine-tune the instructions of the LLM-as-a-judge model integrated into the AI assistant by the developers.

Iteration

We repeat the previous points until the operational effectiveness of the judge LLM reaches the level expected by the client

Incorporating human evaluation

We establish a process in which, based on risk analysis, the less confident LLM-as-a-judge evaluation results are reviewed by human experts, thus contributing to the long-term development of the AI assistant.