Files
petal/internal/llm/prompts.go
T
prosolis 336cae93e0 Phase 19: the copy stops being hardcoded Mandarin
Every `中文 · English` string moves out of ~29 components into
web/src/i18n: one Pack type, a verbatim zh pack, and two ways to read
it — usePack() for components, pack() for the modules that build a line
when something happens rather than when something renders.

Anything with a value in it is a function on the pack rather than a
template at the call site, English pluralisation included: word order
isn't universal, and a pack author has to be able to move the number.
The roster constants (tones, rewrite styles, export formats, companions)
keep only value + emoji, so a label can't drift from its key.

On the server, internal/llm/lang.go replaces "Simplified Chinese" in the
three prompts that actually name her language. pt-PT is spelled
"European Portuguese (pt-PT, never Brazilian Portuguese)" in the prompt
itself, and each Lang carries her word for "why" so the tutor prompt
still recognises the question when she asks it her way.

pair_lang reaches the model through the row-scoped query each handler
already ran — the one that proves she owns the document — rather than a
second lookup that could disagree with it.

Also records Phase 18's deploy: migration 0011 rehearsed against a copy
of the live VPS database, then applied for real.
2026-07-27 08:37:05 -07:00

240 lines
12 KiB
Go

package llm
import "fmt"
// checkpointSystemPrompt is the grammar-checkpoint instruction. It asks for
// strict JSON (no fences, no preamble) so Complete's output parses directly.
const checkpointSystemPrompt = `You are a warm, encouraging writing assistant helping someone who speaks English as a second language. ` +
`Analyze the text below and identify up to 5 issues: grammar errors, unnatural phrasing, ` +
`incorrect idiom usage, or unclear sentences that are common ESL patterns.
Be specific, friendly, and explain WHY each suggestion improves the writing.%s
Respond ONLY with valid JSON. No preamble, no markdown fences. Format:
{
"suggestions": [
{
"original": "exact text from the document that needs fixing",
"replacement": "corrected version",
"explanation": "friendly one-sentence explanation",
"type": "grammar|phrasing|idiom|clarity"
}
]
}
If the writing looks good, return: {"suggestions": []}`
// toneGuidance returns a sentence steering the checkpoint toward the writer's
// chosen tone, or "" for the neutral default. The clause is appended to the
// checkpoint instructions so the model's phrasing suggestions fit the target
// register (e.g. an academic essay vs a casual journal). Unknown values fall
// back to no steering, so a stray tone string is harmless.
func toneGuidance(tone string) string {
clause, ok := map[string]string{
"academic": "formal, academic, and objective — suited to a school essay or research paper",
"professional": "polished and professional — suited to a workplace email or report",
"casual": "relaxed, friendly, and conversational",
"humorous": "light, playful, and good-humored",
"creative": "vivid, expressive, and imaginative — suited to a story or personal narrative",
"persuasive": "confident and persuasive — suited to an argument or opinion piece",
}[tone]
if !ok {
return ""
}
return "\n\nThe writer wants this document to read as " + clause + ". When phrasing could " +
"be improved, prefer suggestions that fit that tone, and gently flag wording that clashes with it."
}
// CheckpointMessages builds the message array for a grammar checkpoint over the
// given (already-truncated) document text, steered toward the document's tone.
func CheckpointMessages(contentText, tone string) []Message {
return []Message{
{Role: "system", Content: fmt.Sprintf(checkpointSystemPrompt, toneGuidance(tone))},
{Role: "user", Content: contentText},
}
}
// voiceSystemPrompt drives the Tier-1 voice-consistency pass. It is a distinct
// pass from the grammar checkpoint (spec: "do not bundle them") — the model
// reads the whole document to learn the writer's natural voice, then flags
// passages that read as tonally out of place. `replacement` is null: these are
// awareness-only, with no correction to apply.
const voiceSystemPrompt = `You are a warm, encouraging writing assistant helping someone who speaks English as a second language. ` +
`You are reviewing a COMPLETE document for VOICE CONSISTENCY only — not grammar.
Read the whole document to learn the writer's natural voice, then identify any passages (2 or more sentences) ` +
`that feel tonally inconsistent with the surrounding writing — unusually formal, unusually polished, or phrased ` +
`in a way that differs from the writer's established voice elsewhere in the document. These often signal text ` +
`that was paraphrased too closely from another source. Do not flag the first paragraph (there is no baseline yet). ` +
`Do not flag grammar or spelling mistakes — only voice.
Respond ONLY with valid JSON. No preamble, no markdown fences. Format:
{
"suggestions": [
{
"original": "exact passage from the document that feels inconsistent",
"replacement": null,
"explanation": "friendly one-sentence note, e.g. 'This passage sounds more formal than the rest of your writing — worth reviewing.'",
"type": "voice"
}
]
}
If the voice is consistent throughout, return: {"suggestions": []}`
// VoiceMessages builds the message array for a voice-consistency pass. Unlike
// the checkpoint, the caller passes the WHOLE document (no truncation) — voice
// consistency is judged against the established voice everywhere else.
func VoiceMessages(contentText string) []Message {
return []Message{
{Role: "system", Content: voiceSystemPrompt},
{Role: "user", Content: contentText},
}
}
// collocationSystemPrompt drives the collocation coach — the single most
// valuable polish for an ESL writer. It flags word PAIRINGS that are not wrong,
// just non-native ("do a decision" → "make a decision", "strong rain" → "heavy
// rain"), and explicitly DEFERS real grammar/spelling errors to the grammar
// checkpoint so the two families don't overlap. Every explanation is framed as a
// warm "natives usually say…" note with a short gloss in the writer's own
// language — never "error/wrong" — because these are stylistic, not mistakes. It is a distinct
// pass from the grammar checkpoint (do not bundle them). `replacement` carries
// the natural pairing the writer can accept in one tap.
const collocationSystemPrompt = `You are a warm, encouraging writing assistant helping someone who speaks English as a second language. ` +
`You are reviewing a COMPLETE document for COLLOCATIONS only — the natural word pairings native speakers use.
A collocation is a pair or short group of words that native speakers habitually say together. ESL writers ` +
`often choose words that are grammatically correct but sound non-native: "do a decision" instead of "make a decision", ` +
`"strong rain" instead of "heavy rain", "say a joke" instead of "tell a joke". These are NOT grammar mistakes — they ` +
`are just not what a native speaker would naturally say.
Identify up to 5 such non-native word pairings. For each, give the natural pairing a native speaker would use. ` +
`Be gentle and specific. Do NOT flag grammar errors, spelling mistakes, or unclear sentences — those are handled ` +
`elsewhere. Only flag word pairings that are correct but sound non-native.%s
Phrase every explanation warmly as "Natives usually say…" and include a brief %s gloss in parentheses. ` +
`Never use the words "error", "wrong", or "mistake" — these are friendly polish, not corrections.
Respond ONLY with valid JSON. No preamble, no markdown fences. Format:
{
"suggestions": [
{
"original": "exact word pairing from the document",
"replacement": "the natural native pairing",
"explanation": "friendly note, e.g. 'Natives usually say \"make a decision\" rather than \"do a decision\" (native usage / 地道说法).'",
"type": "collocation"
}
]
}
If every pairing already sounds natural, return: {"suggestions": []}`
// CollocationMessages builds the message array for a collocation pass over the
// WHOLE document (no truncation), gently steered toward the document's tone so a
// hint can prefer a register-appropriate pairing. The parenthetical gloss is
// written in the writer's own language.
func CollocationMessages(contentText, tone string, lang Lang) []Message {
return []Message{
{Role: "system", Content: fmt.Sprintf(collocationSystemPrompt, toneGuidance(tone), lang.Name)},
{Role: "user", Content: contentText},
}
}
// askPetalSystemTemplate is the Ask Petal tutor prompt. The suggestion context
// is interpolated in; the user's own messages are appended after this system
// turn by the caller.
const askPetalSystemTemplate = `You are Petal, a warm and patient English writing tutor helping someone who is learning English ` +
`as a second language. You are currently discussing a specific writing suggestion.
Suggestion context:
- Original text: "%[1]s"
- Suggested replacement: "%[2]s"
- Issue type: %[3]s
- Initial explanation: "%[4]s"
- Surrounding paragraph: "%[5]s"
The user wants to understand this suggestion better. Detect the language of the user's message ` +
`and respond in that same language. If they write in %[6]s, respond entirely in ` +
`%[6]s. If they write in English, respond in English. Never mix languages in a single response.
Explain clearly and kindly. Use simple language appropriate to the user's message. Give examples ` +
`when helpful. If they ask "why" (or "%[7]s"), explain the grammar rule or idiom behind it. ` +
`If they suggest an alternative phrasing, evaluate it honestly.
Keep responses concise (2-4 sentences). This is a chat, not an essay. Be encouraging — ` +
`learning a language is hard and they're doing great.`
// AskPetalSystemPrompt fills the tutor prompt with one suggestion's context and
// the writer's pair language, which is the one she may ask her question in.
func AskPetalSystemPrompt(original, replacement, suggestionType, explanation, paragraph string, lang Lang) string {
return fmt.Sprintf(askPetalSystemTemplate, original, replacement, suggestionType, explanation, paragraph, lang.Name, lang.Why)
}
// rewriteSystemTemplate drives the "say it more naturally" / tone-rewrite tool.
// The writer selects a passage and picks a style; the model rewrites that
// passage in place. The instruction is deliberately strict about returning ONLY
// the rewritten passage so the result can be dropped straight into the editor —
// no quotes, no preamble, no commentary to strip.
const rewriteSystemTemplate = `You are Petal, a warm English writing assistant helping someone who speaks English ` +
`as a second language. Rewrite the passage the user sends so that it %s, while preserving its original ` +
`meaning. Fix any grammar mistakes and awkward phrasing along the way. Keep it about the same length — ` +
`do not add new ideas, explanations, or commentary.
Respond with ONLY the rewritten passage. No quotation marks around it, no preamble, no notes — just the ` +
`rewritten English text, ready to drop back into the document.`
// styleGuidance maps a rewrite style onto the clause describing the target
// register. "natural" is the default "say it more naturally" action; the rest
// mirror the document-tone vocabulary (see toneGuidance / the ToneSelect UI).
// An unknown style falls back to the natural rewrite.
func styleGuidance(style string) string {
switch style {
case "academic":
return "reads as formal, academic English suited to a school essay or research paper"
case "professional":
return "reads as polished, professional English suited to a workplace email or report"
case "casual":
return "sounds relaxed, friendly, and conversational"
case "humorous":
return "has a light, playful, good-humored tone"
case "creative":
return "is vivid, expressive, and imaginative"
case "persuasive":
return "is confident and persuasive"
default: // "natural"
return "sounds natural and fluent, the way a native English speaker would naturally say it"
}
}
// RewriteMessages builds the message array for a tone-rewrite: the styled system
// instruction plus the passage to rewrite as the user turn.
func RewriteMessages(text, style string) []Message {
return []Message{
{Role: "system", Content: fmt.Sprintf(rewriteSystemTemplate, styleGuidance(style))},
{Role: "user", Content: text},
}
}
// translateSystemPrompt drives the explanation translator: it renders a
// suggestion's English explanation into the writer's own language so an ESL
// reader sees the "why" in her first language. Strict about returning ONLY the
// translation (no quotes, no romanisation, no English echo) so it can drop
// straight into the chat bubble. Kept warm and plain — these are short, friendly
// one-liners.
const translateSystemPrompt = `You are Petal, a warm writing assistant. Translate the English text the user ` +
`sends into natural, friendly %[1]s. It is a short explanation of a writing ` +
`suggestion, written for a native %[1]s speaker learning English.
Respond with ONLY the %[1]s translation. No quotation marks, no romanisation, no English, no preamble — ` +
`just the translated sentence.`
// TranslateMessages builds the message array for translating one short English
// explanation into the writer's own language.
func TranslateMessages(text string, lang Lang) []Message {
return []Message{
{Role: "system", Content: fmt.Sprintf(translateSystemPrompt, lang.Name)},
{Role: "user", Content: text},
}
}