Lora, your AI assistant

Type in plain English — Lora's developer skills turn it into accurate, structured output. Ready to copy.

How it works

  1. Create a free account — get 100,000 tokens per month at no cost. No credit card required.
  2. Describe what you need in plain English — no forms to fill, no dropdowns to configure. The AI understands context.
  3. Select a skill below the input for specialized output — Product Schema, Article Schema, and more coming soon.
  4. Copy the result and paste it directly into your page or codebase.

Why Lora produces better structured data than general AI

General-purpose AI models (ChatGPT, Gemini, Claude) hallucinate on structured tasks — wrong property names, missing required fields, incorrect price formats, properties that do not exist in the schema.org spec. Paste the result into Google's Rich Results Test and it fails.

The reason is that large language models are optimized for breadth — they handle millions of tasks reasonably well, but the tradeoff is precision on structured, rule-bound output. A model trained on everything from poetry to Python cannot reliably follow the exact field names, URI formats, and validation rules of a specific standard like schema.org.

  • No hallucinated fields — the AI only outputs properties that actually exist in the spec
  • Google-compliant output — structured data passes Rich Results Test validation
  • Multi-turn refinement — describe your content, then refine it in follow-up messages
  • Free to start — 100K tokens/month on the free plan, no credit card required
LoraChatGPTGeminiClaude
Skill-oriented assistant✓ Skill-guided output✗ Generic only✗ Generic only✗ Generic only
Skill validation✓ Server-enforced✗✗✗
Specialized skills✓ Schema, SEO (growing)✗ Generic prompting only✗ Generic prompting only✗ Generic prompting only
Free plan100K tokens/monthGPT-3.5 limitedBasic tierVery limited
Image generation✗✓ DALL-E 3✓ Imagen✗
Web / real-time search✗✓✓ Google Search✗
CodeBasicStrongGood✓ Best-in-class
Cost for heavy useLowHighMediumHigh

✓ = strength for that category ✗ = not available or not the right tool

Use Lora when you need structured, validated output from a task-specific skill — no hallucinated fields, no generic guessing. Use ChatGPT for image generation or plugin-extended workflows. Use Gemini when your task requires real-time web research and Google data. Use Claude for complex code review, long-document analysis, or high-quality content writing.

Available AI skills

Select a skill chip below the input to activate specialized mode. Each skill loads a custom AI model and a precise system prompt built for that specific task — the AI stays focused on the task instead of guessing from a generic instruction.

  • Product Schema — guided conversation that collects product data step by step and outputs a complete, valid schema.org/Product JSON-LD block. Covers name, price, availability, brand, ratings, shipping, and return policy. Minimum required for Google Rich Results eligibility: name + price + currency + availability.
  • JSX ↔ HTML — converts React JSX to plain HTML and HTML back to JSX. Auto-detects direction from your input. Handles className/class, camelCase props, style objects, SVG attributes, boolean attributes, fragments, and event handlers. Outputs the full conversion with notes on anything that needed special handling.

Privacy

Your input is sent to the DeepSeek API for processing. Do not include personal data, internal pricing strategy, confidential sourcing, or employee information. Treat Lora like a public search engine — describe your content as it would appear publicly.

Token usage

Token usage is based on actual DeepSeek processing — both your input and the AI's output count. A typical product schema generation uses 300–600 tokens per turn. Free accounts receive 100,000 tokens per month — enough for hundreds of generations. Tokens reset monthly. Check your balance in Profile → Usage.

Frequently Asked Questions

What AI skills are available?
Currently: Product Schema (generates valid schema.org/Product JSON-LD). Article Schema, FAQ Schema, and LocalBusiness Schema AI skills are in development. Select a skill via the chips below the input.
What are tokens and how are they counted?
Tokens are units of text processed by the AI. Both your input and the AI's output count toward your monthly balance. On average, 1 token ≈ 0.75 words. A typical schema generation uses 300–800 tokens.
Do I need an account to use Lora?
Yes — Lora requires a free account because it consumes server-side compute. The free plan gives you 100,000 tokens per month at no cost. No credit card required. All image converters, text converters, and form-based schema generators work without an account.
Does my token balance reset every month?
Yes. Your token balance resets to your plan limit on the same calendar day each month. Unused tokens do not roll over.
Can I refine the output by replying in chat?
Yes. Each conversation keeps history. After the initial generation, reply with adjustments: "change availability to out of stock", "add brand: Samsung", "include free shipping to the US". Each reply updates the result based on the full conversation context.
What happens to my conversations after I close the browser?
Conversations are saved to your account and accessible via the sidebar in AI mode. You can resume any previous conversation or start a new one at any time.
Is the generated schema valid?
The model is prompted to produce schema.org-compliant JSON-LD. Always validate with the Google Rich Results Test or Schema.org Validator before deploying to confirm field accuracy and Rich Results eligibility.
Is my content sent to a third party?
AI inference is handled server-side by our backend via the DeepSeek API. Your input is processed to generate the response. We do not sell, share, or log your content for advertising or training purposes.
What happens if I run out of tokens?
AI generation is blocked until your monthly token balance resets. Check your remaining balance and reset date in Profile → Usage.
How does the AI avoid hallucinating schema fields?
Each skill is built around a detailed system prompt that defines exactly which fields exist in the spec, what values are valid, and the required format for every property — including all schema.org enum URIs. The AI operates within these constraints and cannot invent a field that does not exist. For Product Schema, the system prompt includes the full schema.org/Product definition, all valid enum values with their exact URI format, and validation rules that match Google's Rich Results Test requirements.