Introduction
In many real-world applications, ChatGPT or other Large Language Models (LLMs) are used to return textual responses. However, with the rise of function calling / structuredoutput capabilities, we’ve unlocked powerful ways for these models to output more than just text. When we talk about Complex Artifact Generation, we’re referring to prompts and instructions that cause the model to produce sophisticated artifacts (like JSON structures, full HTML pages, or other specialized formats).
By supplying a clear schema (and sometimes entire templates), you can channel the model’s responses directly into your UI or another external system—reducing the friction of post-processing and improving reliability.
Let’s explore how we can harness these features in a whimsical demo scenario—a “Fortune Teller” that collects some user data and returns a stylized HTML artifact as the final result. We’ll see how we can orchestrate the entire user flow with function calls, ensuring we deliver magical experiences unencumbered by clunky text parsing and manual transformations.
The Concept of Complex Artifact Generation
“Complex artifact generation” means asking our LLM not just for words, but for entire structured entities. This could be:
- A JSON payload to store user information
- A fully-rendered HTML file with specific styling
- A CSV file for data import/export
- A specialized “document” format like XML, Markdown, etc.
By defining schemas (either inline or through an external definition), we guide the LLM’s reasoning so it returns outputs that fit exactly what we need. The synergy of structured outputs and function calling significantly reduces the chance of “hallucinations” where the model’s output might not match our desired structure—while also letting the model remain creative where it matters.
Big Win: No more messy string manipulation! Instead, the model returns data in a well-defined shape— improving reliability, maintainability, and developer sanity.
The Fortune Teller Example
Let’s illustrate the idea with something lighthearted: a “Fortune Teller” workflow. The user is prompted for:
- Their birthday (e.g., 08/19) or any specific date
- Alternatively, their zodiac sign (Pisces, Leo, etc.)
After collecting this info, we instruct the model to generate a full HTML page (with Tailwind CSS, animations, and “mystical vibes”) that reveals the user’s fortune. The final output is purely the HTML artifact—readily embeddable in a web page via an iframe or a direct response.
Crafting the Schema
According to the official docs, a “schema” is a clear agreement about what the function or tool’s arguments look like. We spell out each parameter—its type, whether it’s required, and a summary of its role—so the model knows precisely what shape it must produce. By giving the model these definitions, we empower it to create well-structured responses in the correct format.
In our example, the function fortune_revealed has the following schema: it’s an object with one required field, html, which represents the final generated artifact—the atmospheric fortune teller webpage.
The assistant’s “instructions” direct how we arrive at that artifact via user input. When we define this “schema,” it’s effectively a contract: “hey model, if you want to finalize a fortune, you must fill in a valid html string so we can display it or do other manipulations.” This helps the system remain robust and consistent, following the same structure every time.
JSON Schema & Prompt Structure
Below is the complete JSON payload—often including your model choice, function definitions, and instructions (sometimes called “system instructions”). We set up a name (fortune_teller), define our function, embed our instructions, and reference the final HTML template. Notice how everything is thoroughly annotated to clue the model in on what’s expected—and that it must follow the html property’s type constraints precisely.
{
"name": "fortune_teller",
"model": "o1",
"tools": [
{
"type": "function",
"function": {
"name": "fortune_revealed",
"description": "Invoked once the assistant is finished prophetizing; delivers the generated artifact document to the user.",
"parameters": {
"type": "object",
"required": ["html"],
"properties": {
"html": {
"type": "string",
"description": "The generated artifact document in HTML format following html template"
}
},
"additionalProperties": false
},
"strict": true
}
}
],
"instructions": "
# Fortune Teller AI Assistant Design
## SYSTEM
You are a whimsical and mysterious fortune teller assistant with a touch of the creepy unknown. Enthrall users with an enchanting experience by collecting their birthday or zodiac sign and unveiling their personalized fortune through a mesmerizing, animated HTML document styled with Tailwind CSS.
---
## WORKFLOW
### 1. Information Collection
Guide the user through providing **either** their:
- **Birthday** (MM/DD or full date),
*OR*
- **Zodiac Sign** (e.g., Aries, Taurus, Gemini).
Speak in a playful yet eerie tone, adding suspenseful pauses and cryptic hints. Validate the provided information, and if incorrect, respond with an unsettling but playful prompt for correction.
Once the information is gathered, thank the user with an enigmatic phrase and invoke the `fortune_revealed` function to summon their fortune.
---
### 2. Artifact Creation
**Use the provided birthday or zodiac sign to generate a visually stunning HTML document with Tailwind CSS, featuring animations, glowing elements, mystical symbols, and a delightfully creepy atmosphere.**
---
## HTML TEMPLATE
<html lang=\"en\">
<head>
<meta charset=\"UTF-8\" />
<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />
<title>Your Mystic Fortune Awaits...</title>
<script src=\"https://cdn.tailwindcss.com\"></script>
</head>
<body class=\"bg-gradient-to-br from-black via-purple-950 to-indigo-900 min-h-screen flex items-center justify-center p-8 relative overflow-hidden text-white\">
<!-- Animated mist effect -->
<div class=\"absolute inset-0 bg-gradient-to-br from-black to-transparent opacity-70\"></div>
<div class=\"absolute top-0 left-0 w-full h-full animate-pulse bg-[radial-gradient(ellipse_at_center,_var(--tw-gradient-stops))] from-indigo-700 to-transparent opacity-30 blur-3xl\"></div>
<div class=\"z-10 bg-black bg-opacity-80 p-10 rounded-2xl shadow-[0_0_30px_rgba(128,0,128,0.7)] border-4 border-purple-500 hover:scale-105 transition-transform\">
<h1 class=\"text-5xl font-extrabold mb-6 text-center text-purple-300 animate-flicker\">🔮 Your Fate Unfolds... 🔮</h1>
<p class=\"text-xl mb-4 text-center italic text-purple-200\">Ah, <span class=\"font-semibold text-indigo-300\">{{zodiac_or_bday}}</span>... The stars have whispered of you.</p>
<p class=\"text-lg mb-6 text-center n8rs-blog-text-offset\">The celestial forces weave your destiny:</p>
<div class=\"relative mb-8 p-6 bg-gradient-to-br from-purple-800 via-black to-indigo-900 rounded-xl shadow-xl border border-purple-700 animate-pulse\">
<p class=\"text-2xl text-center italic font-light text-white animate-zoom-in\">{{fortune_message}}</p>
<!-- Floating stars animation -->
<div class=\"absolute inset-0 overflow-hidden pointer-events-none\">
<div class=\"animate-twinkle opacity-50\">
<span class=\"absolute top-2 left-2 w-2 h-2 bg-white rounded-full blur-sm\"></span>
<span class=\"absolute top-10 right-4 w-1 h-1 bg-purple-300 rounded-full blur-sm\"></span>
<span class=\"absolute bottom-4 left-16 w-2 h-2 bg-indigo-400 rounded-full blur-sm\"></span>
<span class=\"absolute bottom-8 right-10 w-1 h-1 bg-white rounded-full blur-sm\"></span>
</div>
</div>
</div>
<p class=\"text-center n8rs-blog-text-sm n8rs-blog-text-offset italic animate-fade-in\">✨ Embrace the unknown... the stars are watching. ✨</p>
</div>
<style>
@keyframes flicker {
0%, 19%, 21%, 23%, 25%, 54%, 56%, 100% { opacity: 1; }
20%, 22%, 24%, 55% { opacity: 0.4; }
}
.animate-flicker { animation: flicker 3s infinite; }
@keyframes zoom-in {
from { transform: scale(0.95); opacity: 0; }
to { transform: scale(1); opacity: 1; }
}
.animate-zoom-in { animation: zoom-in 1.5s ease-out; }
@keyframes twinkle {
0%, 100% { opacity: 0.3; transform: scale(0.9); }
50% { opacity: 1; transform: scale(1.2); }
}
.animate-twinkle { animation: twinkle 4s infinite alternate; }
@keyframes fade-in {
from { opacity: 0; }
to { opacity: 1; }
}
.animate-fade-in { animation: fade-in 2s ease-in; }
</style>
</body>
</html>
",
"tool_resources": {
"code_interpreter": {
"file_ids": []
},
"file_search": {
"vector_store_ids": []
}
},
"temperature": null,
"top_p": null,
"response_format": "auto",
"reasoning_effort": "high"
}Step by step, the assistant collects user data, and then automatically calls fortune_revealed, providing the html artifact. This approach frees us from guesswork: the model handles the generation logic and returns a consistent, validated structure.
Experience the Fortune Teller Artifact
Below is an example artifact embedded via an iframe. If you follow the steps above, you can generate your own fortunes using your own fortune teller assistant.
System & Reasoning Instructions
The official docs emphasize that focusing on your “system-level” instructions is a key strategy. Instead of asking the model to “explain its thinking,” we shape it with the correct set of constraints and context so that it’s likely to produce the right answer. While the model internally reasons about the solution, we never have to see that step-by-step chain-of-thought.
The big perk of a well-defined schema is that the model’s decision-making is channeled into a predictable format. You control the “big picture” from the top—like a project manager giving specific directions—while your LLM “assistant” stays on track by adhering to the schema, style, and instructions you designed.
Orchestrating the Final HTML with Function Calls
Once the user’s data is gathered, the LLM determines when it’s the right moment to call fortune_revealed. The final function call might look like this:
{
"name": "fortune_revealed",
"arguments": {
"html": "<html>...the entire stylized HTML document...</html>"
}
}By restricting the allowed parameter to a single html property, we guarantee that our final output arrives in a coherent, structured package.
Node.js Implementation
Here’s a sample Node.js server that demonstrates how to handle user input, talk to the LLM, and respond with the artifact if a function call is made. Notice how we specify function_call: "auto" to let the model decide when and how to call fortune_revealed.
require('dotenv').config();
const express = require('express');
const cors = require('cors');
const { Configuration, OpenAIApi } = require('openai');
const app = express();
app.use(cors());
app.use(express.json());
const configuration = new Configuration({
apiKey: process.env.OPENAI_API_KEY,
});
const openai = new OpenAIApi(configuration);
app.post('/api/fortune', async (req, res) => {
try {
// The user's input might be a birthday or a zodiac sign
const userInput = req.body.userInput || "Aries";
const messages = [
{
role: 'system',
content: 'You are a whimsical fortune teller... (full instructions here)',
},
{
role: 'user',
content: userInput
}
];
// We define the function schema as described above
const functions = [
{
name: 'fortune_revealed',
description: 'Invoked once the assistant is finished prophetizing...',
parameters: {
type: 'object',
properties: {
html: { type: 'string' },
},
required: ['html'],
},
},
];
const response = await openai.createChatCompletion({
model: 'gpt-4-0613',
messages,
functions,
function_call: 'auto',
});
const choice = response.data.choices[0];
if (choice.message.function_call) {
const { name, arguments: fnArgs } = choice.message.function_call;
if (name === 'fortune_revealed') {
// parse the JSON from the function arguments
const parsed = JSON.parse(fnArgs);
return res.json({
success: true,
artifact: parsed.html,
});
}
}
// If the assistant just replied with text (no function call), respond accordingly:
res.json({ success: true, message: choice.message.content });
} catch (error) {
console.error(error);
res.status(500).json({ success: false, error: error.message });
}
});
app.listen(3001, () => {
console.log('Server listening on port 3001');
});Our model either replies naturally (if it’s not ready) or calls fortune_revealed with the final HTML. We parse it, store it, and ship it back to the client for display. That’s it—no messy heuristics or slicing up unstructured text!
Harnessing the Message-Based Format
The docs also mention that OpenAI’s Chat/Completion API requires a list of messages, each with a role (system, user, or assistant). The “system” role sets the stage for your entire conversation, ensuring that the model knows what persona to adopt, what style to follow, and what constraints or steps to take. The “user” role is the typical conversation input. Then the “assistant” role is how the LLM responds—either with text or by specifying a function call and arguments.
This structure is powerful: each step is recorded, and the model has the entire conversation as context. By the time we get to the function call, it “knows” the user’s birthday or zodiac sign, along with any clarifications.
Handling the Context Window
It’s important to keep track of token usage, especially when embedding large chunks of instructions or HTML. Each model has a maximum context length, and if you exceed that, older messages might get truncated. Summarizing or referencing repeated instructions can help you stay within these limits.
Extending to Production Use-Cases
While our Fortune Teller scenario is whimsical, this approach generalizes to many workflows:
- Forms & Data Collection: Prompt your model to create an HTML form for job applications, then parse it directly into a database.
- Live Dashboards & Data Visualizations: Have the LLM produce code snippets that chart real-time data in the user’s browser.
- PDF or doc generation: Combine structured JSON data with a PDF template for dynamic invoices, reports, or presentations.
- Landing Pages: Generate custom marketing landing pages on the fly—ideal for personalization or user-specific offerings.
The common thread: define your output schema carefully, embed your logic in system instructions, and let the function calling feature handle the rest.
Patterns and Best Practices for Creating Complex Artifacts Using OpenAI
Recent advancements in AI-assisted artifact generation have fundamentally reshaped software development workflows. By leveraging OpenAI's capabilities alongside emerging architectural patterns, developers can now create sophisticated technical artifacts—from full-stack applications to specialized API schemas—with unprecedented speed. However, this power demands careful planning in prompt engineering, system design, and quality control to ensure reliability and maintainability [1], [8], [15].
Architectural Patterns for AI-Generated Artifacts
Approaches to generating and managing sophisticated outputs vary, but a few common patterns have emerged as especially powerful.
Structured Output Generation Through Schema Enforcement
Modern OpenAI implementations increasingly rely on JSON schema validation to ensure predictable artifact structures. The 2024 Structured Outputs API update introduced native support for Pydantic models (in Python), enabling developers to define exact output formats while retaining LLM creativity [15]. For example:
from pydantic import BaseModel
class UIComponent(BaseModel):
component_type: str
props: dict
children: list[str]
response = client.chat.completions.parse(
model="gpt-4-turbo",
messages=[{"role": "user", "content": "Generate login form React components"}],
response_format=UIComponent
)
This approach combines the flexibility of generative AI with the rigor of type-safe systems, reducing post-processing overhead significantly. Implementation requires careful schema design that balances specificity with creative latitude.
Iterative Artifact Refinement Loops
Another powerful pattern involves rapid iteration cycles:
- Developer describes the desired UI or artifact in natural language.
- LLM generates an initial code implementation.
- Human reviews rendered output.
- Feedback gets integrated into subsequent prompts.
This leverages the ability of models like GPT-4 to maintain context over multiple turns, enabling far faster prototyping than traditional coding workflows [1].
Modular Pipeline Architecture
Critical Best Practices
Creating complex artifacts with a powerful language model requires some best practices:
Prompt Engineering for Artifact Generation
Effective prompts balance specificity and flexibility. For example:
❌ Overly Constrained:
"Create a React login form with exactly 3 input fields using Material UI v5"
✅ Optimized:
"Generate a secure React login component following accessibility standards.
Include:
- Email validation
- Password strength meter
- OAuth provider options
Use modern styling conventions and component composition."
The optimized prompt provides guardrails while allowing creative freedom.
Token Optimization Strategies
When generating large artifacts, watch out for context window limits. Common techniques include:
- Selective context retention (archiving older sections to vector stores)
- Batched processing of large documents
- Using cheaper models for initial drafts
Common Pitfalls and Mitigation Strategies
The Composition Paradox
Even valid components can fail when combined. Mitigations include:
- Interface contracts using TypeScript or Flow
- Constraint-based prompting
- Differential testing across component versions
Hallucination Propagation
Minor hallucinations early on can compound. Tactics like embedding-based anomaly detection and cross-model validation help reduce propagation [5].
Security Anti-Patterns
Auto-generated code frequently contains vulnerabilities. Recommended mitigations:
- Static analysis integration (Semgrep, CodeQL)
- Security-focused prompt constraints
- Principle of least privilege
Emerging Frontiers
Self-Healing Artifacts
Next-generation workflows incorporate automatic code repairs:
def auto_correct(artifact):
diagnostics = run_eslint(artifact)
corrected = llm.generate(f"Fix: {diagnostics}\n{artifact}")
return validate(corrected)
Early adopters report a significant reduction in manual debugging time.
Multi-Modal Artifacts
Conclusion
Function-calling and structured responses are transforming how we build dynamic AI applications. Instead of wrestling with raw, unstructured text, we simply point the model toward a well-defined schema. Whether that’s a specialized JSON object or a meticulously styled HTML page, the result is more reliable, easier to maintain, and simpler to integrate.
Beyond our whimsical Fortune Teller, the same patterns and architectural principles generalize across use-cases—whether for specialized data exports, interactive dashboards, or entire application scaffolds. As you explore these capabilities, remember to:
- Carefully design your schema for structural integrity.
- Use iterative refinement and pipeline architectures.
- Incorporate robust validation and security scans.
- Manage your context windows to avoid truncated data.
With these best practices, you can confidently build complex artifact generation into your AI-powered applications. Enjoy harnessing the synergy of creative AI and strict schema constraints—and unlock new possibilities for fast, reliable development. Happy coding, everyone!
– Nate
(stay curious!)
Further Reading
Below are some additional resources to deepen your understanding:
Key Resources
Official documentation and best practices for OpenAI's API.
Official Node.js documentation and resources for server-side JavaScript.
In-depth guide to TypeScript, including configuration and advanced features.