Hungarian-language prompt engineering

DEFINITION

Instructing AI models (ChatGPT, Claude, Gemini, Mistral) in Hungarian is different from English. Hungarian linguistic specifics affect output: longer sentences, more suffixes, Hungarian terminology, local context. When building Hungarian AI systems: prompts must be tested in Hungarian on a Hungarian test set. Common errors: model returns English instead of Hungarian if not flagged, Hungarian quotation marks (»...«, „...”) get mixed, Hungarian data formats (date, phone) sometimes wrong. A 50-200 Hungarian Q&A test set is mandatory — we build them for HUF 200-500k.

RELATED TERMS06

RAG (Retrieval-Augmented Generation)→
An AI architecture where the model retrieves relevant documents from your own data before answering, and only reasons over that context. Kills ~80% of hallucinations.
LLM (Large Language Model)→
A neural model with billions of parameters (GPT-4, Claude, Mistral) that generates text. In production we never use one bare · always wrapped in retrieval and guardrails.
Embedding→
A vector representation of text (e.g. 1536 floats). If two embeddings are close, the meanings are close. In RAG we use this to pick relevant chunks.
Vector database→
A database specialised for fast approximate-nearest-neighbour search over embedding vectors (pgvector, Qdrant, Weaviate). The engineering base of RAG retrieval.
Eval (LLM evaluation)→
An automated test suite that runs ~50–200 'golden' questions against the model before every release and checks that quality metrics (accuracy, factuality, latency) clear the threshold.
Guardrail→
An input- or output-layer that filters the model's prompt/response (PII scrubbers, prompt-injection detectors, JSON-schema validation, topic blocks). Not before/after the model · around it.

MENTIONED IN THE BLOG08