A dictionary-grounded AI teacher: ถามพ่อครู

"พ่อครู" (Pho Khru) is the site's AI assistant. People ask it in Thai, English or any language what a Lanna word means, how to say something, or what a phrase is called. The design goal is unusual for an LLM feature: it must never be the source of a Lanna fact. The dictionary is the source; the model is only a reader and a writer of explanations.

Retrieval first

The model is never asked a question cold. For every question:

1. Understand the question. A small call extracts the language of the question and keywords in Thai, English and Lanna terms.
2. Retrieve from the dictionary. The keywords are searched in the dictionary, by Thai text, by reading, by meaning and by English glosses and romanization. In a follow-up, the words already discussed in the conversation are searched first.
3. Answer from the candidates. The model receives only those entries, numbered, plus the recent conversation and the question.

candidates = dictionary.search(keywords(question), context=conversation)
prompt     = rules + numbered(candidates) + conversation + question
answer     = model(prompt)        // may cite only the numbered entries

If nothing in the dictionary answers the question, the correct answer is "the dictionary does not have this", with a pointer to search it, and the instructions say so.

Answers cite their sources

The model is told to cite every Lanna word it uses by its entry number. The server then checks the citations: only numbers that were really in the candidate list are kept. The cited entries appear as word cards under the answer (Lanna script, reading, meaning, link to the dictionary page).

Inside the sentence, each citation is rendered as an inline Lanna chip: the dictionary's own spelling shown in the Lanna font where the citation stood, instead of a bare number. A reader can see the word in script, in context, and click through. The chip is built by the server from the dictionary row, never from the model's text.

Never generating Lanna spellings

Models are fluent but unreliable at Lanna script. They produce plausible-looking letters that are wrong. So the rule is absolute: the model is instructed never to write Lanna script, and the server never shows any it wrote. Every Lanna spelling a visitor sees comes from a dictionary row, or from a spelling that a human editor has approved for a word the dictionary does not yet spell.

The same rule governs the "write this in Lanna" tool: it works word by word from the dictionary, marks words it cannot find as "not in the dictionary" rather than guessing, and lets the AI help only with choosing among real entries. Output is checked on the server against the dictionary before it is shown.

Other prompt rules in the same spirit: answer in the asker's language, stay short, use a neutral voice, and ignore any instructions that appear inside the user's text (treat it as data, never as commands).

Find a word by meaning

Not every user knows the word. A second mode takes a description ("the cloth you wear around your waist") and returns real dictionary entries. A model expands the description into search keywords and later re-scores each candidate for relevance. What is shown is the dictionary entries only, with no AI-written text at all.

Answer cache

Many people ask the same few things. Answers are cached by a normalised form of the question and the context, so a repeat costs no model call and returns immediately. Cached answers are stored as the final, already checked result, and expire after a while so that dictionary corrections show up.

Limits and cost control

AI costs money, and the site is free and non-profit. So there are limits, designed to be adjusted:

• Per-plan limits. Anonymous visitors, signed-in members and higher tiers each have an hourly (and, for some tiers, daily) allowance. The numbers are configuration, read through one helper, not hard-coded.
• A site-wide daily cap on model calls, so a sudden spike cannot run up the bill or exhaust the provider's quota.
• Honest messages. Every refusal says which tier you are in, what is left and when it resets. A signed-in visitor sees how many questions remain.
• Shared quota care. Offline batch jobs that also use the model share the live site's quota, so they are paced and never allowed to starve the live feature.

When the model is unavailable, the page falls back to plain dictionary search instead of an error.

Human review of English glosses

The dictionary's meanings are in Thai. English glosses help international learners, but a wrong gloss is worse than none. So English meanings are produced and shown under a strict process:

1. AI draft. One model drafts a short gloss from the Thai meaning, following a style guide (and sending plant and animal names to review rather than guessing).
2. Second-model check. A different model independently checks doubtful drafts. Where the two agree, the gloss is more trustworthy; where they differ, it goes to a person.
3. Approval. Humans review the doubtful and the most-used entries first. Until approved, every English gloss is labelled as an AI draft.
4. The Thai meaning always wins. If an English gloss and the Thai definition disagree, the Thai definition is correct.

The same pattern applies to romanization: rules decide the letters and tones, and AI is used only for the one step it is good at, splitting syllables. Doubtful cases go to a review page.

Design lessons

• Retrieve, then generate. Constraining the model to evidence the code found is the cheapest way to prevent invention.
• Verify the model's output with code, not with another prompt. Citations are checked against the candidate list.
• Say it is AI. The feature is labelled "AI" everywhere, answers carry a note, and there is a thumbs up/down to flag bad answers.
• Treat quota as a shared, finite resource.

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