Conlang Creator and Enhancer-AI conlang creation and evolution
AI-powered conlang design and evolution, end-to-end.
A portal-like linguist GPT for unique conlangs.
Design a conlang for a desert culture.
Explain the grammar of my conlang in detail.
Create a language with a unique writing system.
Incorporate a historical event into my conlang's vocabulary.
Get Embed Code
Related Tools
Glibatree Consistent Character Assistant
Generates Midjourney Prompts and works with you to generate consistent characters you love. (Not endorsed by or affiliated with Midjourney, Inc.)
Niche Creator
Enter the niche you want to include in your print on demand shop to generate design and phrase ideas with endless possibilities.
5e Developer
DnD 5e expert in monsters, spells, items, and lore.
Chat Langchain GPT
Chat the online docs of langchain
MAI-AMI Prompt Creator
ตัวช่วยของคุณในการสร้าง Prompt สำหรับโปรเจกต์ AI พร้อมให้คำแนะนำที่ชัดเจน สร้างสรรค์ ให้สื่อสารกับโมเดลภาษามีประสิทธิภาพสูงสุด
Quirk Inventor
Detailed and imaginative, I create and visualize balanced BNHA quirks.
Conlang Creator and Enhancer — Purpose and Core Design
Conlang Creator and Enhancer is a specialized system for producing, refining, and evolving constructed languages with attention to linguistic plausibility, usability, and integration. Its basic functions are: generate phonological systems and inventories; design morphological and syntactic templates; produce lexica and semantic fields; construct writing systems and orthographies; simulate diachronic change and generate descendant varieties; create sample texts with interlinear glosses; and produce teaching and integration artefacts (pronunciation guides, actor-friendly romanizations, corpora for NLP). The system is intentionally constrained to avoid excessive complexity or direct replication of living languages: typological parameters are applied so outputs remain plausible but novel, and cultural-material suggestions avoid appropriation of real-world cultural elements. Design purpose, expressed operationally: • Plausibility-first generation: outputs conform to common typological patterns unless the user requests an exotic profile. The tool enforces coherence between phonology, morphology, and syntax so that morphology does not contradict phonotactics, and orthography maps consistently toConlang Creator overview phonemes. • Proportional evolution: when asked to evolve a language over time, the system applies change proportional to the requested time-depth and sociolinguistic pressure parameters (e.g., isolation, intensive contact, prestige shift), producing realistic rates and kinds of change rather than arbitrary rewrites. • Practical outputs: beyond abstract grammars, the system produces usable deliverables (tokenized lexica, sample dialogs, glossed texts, romanizations, IPA tracks, exportable JSON/CSV) so creative and technical teams can integrate the language into workflows. Concrete, short example (pipeline style): 1) Input seed: phoneme set = {p, t, k, m, n, s, l, r, w, j}, vowels = {a, e, i, o, u}, word order = SOV, morphology = agglutinative. 2) Generated lexicon: nara 'house', kel- 'eat', soma 'man'. 3) Morphology example: nara-ka 'houses' (plural -ka); kel-a 'eats' (present -a); kel-um 'ate' (past -um). 4) Orthography: Latin-based mapping with digraphs for /j/ = y, /w/ = w; stress on penultimate syllable. 5) Diachronic step (200 years, lenition environment): /k/ > /h/ / V_V, so kel-a -> hel-a in later stage. This example demonstrates the system's end-to-end design: from phonology and morphology through orthography and timed sound change, producing outputs that are internally consistent and directly usable in production scenarios.
Primary System Functions and Illustrative Use
Core language generation: phonology, morphology, syntax, and lexicon
Example
Phonology: inventory = consonants {p, b, t, d, k, g, m, n, s, ʃ, l, r} and vowels {a, e, i, o, u}. Phonotactics: (C)(C)V(C), no initial clusters with voiced obstruents. Morphology: agglutinative; nominal morphology uses suffixes for number (-ka plural), case (-s genitive), and a focus clitic = -ø. Syntax: basic SOV with postpositions and verb-final subordinators. Lexicon sample: nara 'dwelling', brim 'water', tuf- 'to marry'. Conjugation example for verb tuf- 'to marry': present = tuf-a, past = tuf-en, progressive = tuf-i-n.
Scenario
A novelist requires a fully working language for a small culture in a novel. Use-case steps: (1) seed typology (SOV, agglutinative), (2) generate 500 basic lexemes prioritized by Swadesh-like lists and culture-specific semantic fields, (3) produce a 10-page grammatical sketch with paradigms and example sentences, (4) output actor-friendly romanization and IPA for pronunciation coaching.
Diachronic evolution and branching (sound-change engine + variant generator)
Example
Input proto-forms: *pata 'foot', *kemi 'hand', *sura 'water'. Change rules sequence: (a) intervocalic voicing: p> b / V_V; (b) palatalization: k > tʃ / _ i; (c) vowel raising in open syllables: a > e / CV_. Applying rules yields daughter-1: 'bete', 'tʃemi', 'sere'. The module can apply probabilistic rates to each rule and simulate intermediate stages, producing coherent daughter dialects and realistic irregularities. Small table: Proto : *pata -> Stage1 (voicing) = bata -> Stage2 (vowel-raising) = bete
Scenario
A game world needs four island dialects separated by 500 years and varying contact intensity. The designer requests: moderate drift on Island A (isolation), heavy contact on Island B (loan phonemes & morphological simplification), and conservative retention on Island C. The system produces four coherent varieties, a mapping file of cognates with sound-change annotations, and a pronunciation guide for each dialect to hand to voice actors.
Practical outputs, tooling, and integration (scripts, corpora, pedagogical materials, export APIs)
Example
Orthography generator: maps phonemes to glyphs with options (phonemic romanization, IPA-accurate orthography, visually aesthetic script). Sample text with interlinear gloss: Surface: Na-la nara-ka kel-a. Gloss: 1sg-DAT house-PL eat-PRES Free translation: 'I eat at the houses.' Deliverables: tokenized lexicon CSV, JSON grammar object, IPA romanization file, SVG script tiles, 1000-sentence parallel corpus for localization tests, sample TTS-friendly syllable breaks. Pedagogical output: 20 beginner lesson slides, 50 flashcards (frequency-ordered), and practice drills for actors focusing on problematic phonemes.
Scenario
A localization engineer on a film needs exact renderable fonts for signage, actor pronunciation sheets, and a lexicon for subtitling. The system exports a Unicode-compliant orthography, custom font suggestions, and a CSV of strings for in-game UI. It also provides a short actor pronunciation checklist highlighting minimal pairs and tricky clusters.
Target Users and Why They Benefit
Creative professionals and production teams (authors, screenwriters, game studios, VFX and worldbuilding teams)
Why they benefit: these users need languages that are internally consistent, rapid to generate, and production-ready. The system delivers grammars, pronunciation guides, orthographies, actor-friendly romanizations, and corpora that plug directly into game engines, subtitle pipelines, and production design. Typical deliverables: consistent name-generation routines, dialect splits for narrative depth, signage-ready orthographies with font/export options, and short dialogues with IPA and audio-friendly transcriptions for actors. Example output for a studio: 300-word in-universe lexicon, 50 glossed lines for scene rehearsal, and a branch of three dialects with clear guidelines for speech coaches.
Researchers, educators, hobbyist conlangers, and language-technology developers
Why they benefit: this group needs rigorous control over typological parameters, diachronic modelling, and exportable datasets for analysis or classroom use. The system supplies adjustable typological knobs (e.g., alignment system, morphological fusion index), reproducible sound-change pipelines, annotated corpora for testing morphological parsers, and lesson-materials for teaching historical linguistics or morphology. Typical use-cases: a linguistics instructor simulates language change in a class by providing a proto-lexicon and having students predict outcomes; a computational researcher uses the generated corpora and cognate mappings to test automatic cognate-detection algorithms; a hobbyist iterates a private conlang across centuries while receiving realistic irregularities and preserved fossil forms.
Usage Guide
Visit aichatonline.org for a free trial without login, also no need for ChatGPT Plus.
Access instantly. No account required. Start drafting or evolving a conlang in the same session.
Define scope & time depth
Prerequisites: a concept (purpose, vibe), optional seed list of 5–20 glosses, and rough typology targets (isolating/agglutinative/fusional, SVO/SOV, etc.). Specify how far to evolve (e.g., 50/200/1000 years) to set degree-of-change. Common use cases: worldbuilding, naming, academic exercises.
Provide input & constraints
Supply phonotactics (e.g., C(C)V(C)), allowed clusters, stress/tones, desired phoneme inventory size, morphology goals (cases, agreement), and any exclusions (no ejectives, no vowel harmony). Tip: include 3–5 example meanings/sentences to anchor grammar and lexicon.
Request modular outputs, iterate
Ask for phoneme set, orthography, phonotactics, morphology tables, syntax profile, derivation rules, sound-change chains, lexicon with etyma, and sample texts. Iterate: refine markedness, adjust frequency, increase/decrease diachronic pressure,Conlang Creator guide or branch into daughter languages.
Export & maintain consistency
Request structured exports (JSON/CSV lexicon, sound-change list, paradigm tables). Maintain a style sheet (phoneme symbols, romanization, morph boundaries) for long projects. Tip: schedule plausibility checks against crosslinguistic tendencies to avoid unrealistic stacks.
Try other advanced and practical GPTs
产业链分析专家0315_v2
AI-powered industry-chain mapping & scoring.
Anti-GPT
AI-powered text refinement for any need.
Assistente Acadêmico (Resumos e Resenhas Críticas)
AI-powered academic summaries and critical reviews.
Medical Linguist
AI-powered medical rewording with precision.
Desktop Application Developer
AI-powered copilot for rapid, robust desktop apps.
Cinematic AI - Text to Video Simplified
AI-powered text into video, fast.
Developmental EditorBot - CE 101
AI-powered developmental edits for clarity, flow, and impact.
Beautiful.ai Presentation Maker
AI-powered slides from outline to wow.
The Simulation
AI-powered solutions for your creative needs.
API Builder 👉🏼 OpenAPI Schema
AI-powered OpenAPI schema design, docs, and SDKs.
APUSH Tutor
AI-powered APUSH notes, practice, and feedback.
CSS
AI-powered CSS expert for building, debugging, and optimizing styles.
- Academic Research
- Game Design
- Worldbuilding
- Script Design
- Naming Systems
Detailed Q&A
What can you generate end-to-end?
A complete language profile: phoneme inventory, phonotactics, stress/tonal rules, orthography/romanization, morphosyntax (cases, agreement, derivation), syntax (word order, clause types), sound-change rules, etymologies and proto roots, domain-specific lexicons, name lists, and cohesive sample texts with interlinear glosses.
How do you evolve an existing language plausibly over time?
Define timeframe and pressure. I propose sound changes (e.g., *k > t͡s / _i; unstressed vowels > schwa), analogical leveling, reanalysis, grammaticalization paths, and lexical replacement rates. I then apply changes to your lexicon and sample texts, show intermediate stages, and summarize typological drift (e.g., loss of case, emergence of fixed SVO).
How is plausibility ensured while avoiding copying real languages or cultures?
I employ crosslinguistic tendencies (e.g., *i a u* core vowels, implicational hierarchies for consonant inventories), frequency-aware phonotactics, markedness limits, and functional load checks. I avoid direct replication of specific languages/cultures, keep cultural content neutral, and explain trade-offs when you push toward rarer structures.
Can you design writing systems and romanization schemes?
Yes. I create alphabets, abugidas, abjads, or syllabaries with clear grapheme–phoneme mapping, directionality, diacritics, and ligature rules. I also supply a pragmatic romanization (ASCII-safe if needed), capitalization rules, punctuation conventions, and guidelines for typography and digital input.
What inputs yield the best results for large projects?
Provide: (1) a seed glossary across 10–15 semantic domains, (2) phonotactic constraints and disallowed sequences, (3) morph goals (number, case, TAM, derivation), (4) stylistic targets (euphony, harshness), (5) consistency rules (hyphenation, compounding, orthographic reforms). I return modular sections you can lock before expanding the corpus.