# Supported Backend Frameworks for LLM Generation in exercises-dataset: 6 Frameworks Explained

> Explore six backend frameworks like Express.js FastAPI ASP.NET Core Spring Boot Laravel and Gin for LLM generation in exercises-dataset. Choose the best fit for your project.

- Repository: [Hasan Emir Yıldırım/exercises-dataset](https://github.com/hasaneyldrm/exercises-dataset)
- Tags: how-to-guide
- Published: 2026-08-01

---

**The exercises-dataset repository supports six backend frameworks for LLM generation: Express.js (Node.js), FastAPI (Python), ASP.NET Core (C#), Spring Boot (Java), Laravel (PHP), and Gin (Go).**

When building REST APIs from the exercises dataset using Large Language Models, selecting the right backend framework determines your language ecosystem, package dependencies, and deployment workflow. The repository's **Ask Your LLM** feature in [`setup.html`](https://github.com/hasaneyldrm/exercises-dataset/blob/main/setup.html) automates prompt generation with framework-specific metadata, enabling any LLM to produce runnable code tailored to your stack.

## Where Framework Support Is Defined

Framework specifications live in the **setup.html** file as a JavaScript object called `FRAMEWORK_META`. Located at lines 78-86, this object maps framework identifiers to their configuration:

```javascript
// From setup.html#L78-L86 - simplified structure
const FRAMEWORK_META = {
  express: {
    name: "Express.js",
    lang: "JavaScript",
    pkg: "express, pg / mysql2 / better-sqlite3",
    run: "node index.js"
  },
  fastapi: {
    name: "FastAPI", 
    lang: "Python",
    pkg: "fastapi, uvicorn, sqlalchemy, psycopg2-binary",
    run: "uvicorn main:app --reload"
  }
  // ... additional frameworks
};

```

This metadata feeds directly into the `buildLlmPrompt()` function (lines 95-112), which constructs the complete prompt sent to your LLM.

## Complete Framework Reference Table

| Framework | Language | Required Packages | Run Command |
|-----------|----------|-------------------|-------------|
| **Express.js** | JavaScript (Node.js) | `express`, `pg` / `mysql2` / `better-sqlite3` | `node index.js` |
| **FastAPI** | Python | `fastapi`, `uvicorn`, `sqlalchemy`, `psycopg2-binary` | `uvicorn main:app --reload` |
| **ASP.NET Core** | C# | `Npgsql` / `MySql.Data` / `Microsoft.Data.Sqlite` | `dotnet run` |

| **Spring Boot** | Java | `spring-web`, `spring-data-jpa`, database driver | `mvn spring-boot:run` |
| **Laravel** | PHP | `laravel/laravel`, database driver | `php artisan serve` |
| **Gin** | Go | `gin-gonic/gin`, `database/sql` + driver | `go run main.go` |

Database drivers adapt automatically based on your selected database engine (PostgreSQL, MySQL, or SQLite).

## How LLM Prompt Generation Works

The **supported backend frameworks for LLM generation** integrate into a unified pipeline. When you select a framework and database in the UI, `buildLlmPrompt()` assembles:

- **Dataset description**: 1,324 exercises with multilingual fields and media paths
- **SQL schema**: Generated from the `DB_SQL` object for your chosen database
- **Required endpoints**: GET `/exercises/:id`, GET `/exercises`, plus filtering and pagination
- **Technical requirements**: Environment-variable DB connections, parameterized queries, CORS, validation, error handling, logging
- **Framework packages**: Inserted via `${fw.pkg}` template substitution

### JavaScript: Generating the Prompt

```javascript
// From setup.html - selecting framework and building prompt
const fwKey = 'express';      // or 'fastapi', 'aspnet', 'spring', 'laravel', 'gin'
const dbKey = 'postgresql';   // or 'mysql', 'sqlite'

const prompt = buildLlmPrompt(fwKey, dbKey);
console.log(prompt);          // Copy this to ChatGPT, Claude, or Gemini

```

### Python: Calling the LLM with Generated Prompt

```python
import openai

# The prompt variable contains the framework-specific instructions

response = openai.ChatCompletion.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": prompt}],
)
generated_code = response.choices[0].message.content
print(generated_code)

```

### Go: Accessing Framework Metadata Directly

```go
package main

import "fmt"

type Framework struct {
    Name string
    Lang string
    Pkg  string
    Run  string
}

var FRAMEWORK_META = map[string]Framework{
    "gin": {
        Name: "Gin (Go)",
        Lang: "Go",
        Pkg:  "gin-gonic/gin, database/sql + db driver",
        Run:  "go run main.go",
    },
    // Additional frameworks...
}

func main() {
    fw := FRAMEWORK_META["gin"]
    fmt.Printf("Framework: %s, Language: %s\n", fw.Name, fw.Lang)
    fmt.Printf("Packages: %s\nRun: %s\n", fw.Pkg, fw.Run)
}

```

## Framework Selection Criteria

Choose your **supported backend framework for LLM generation** based on:

- **Team expertise**: Match your existing language proficiency
- **Deployment target**: Node.js and Python suit serverless; Java and C# excel in enterprise containers

- **Performance needs**: Go (Gin) and Rust-adjacent compiled languages offer lowest latency
- **Ecosystem maturity**: Express.js and Laravel provide extensive middleware ecosystems

The repository's prompt engineering ensures generated code follows idiomatic patterns for each framework regardless of your choice.

## Key Files Supporting LLM Framework Generation

| File | Path | Purpose |
|------|------|---------|
| **setup.html** | [`/main/setup.html`](https://github.com/hasaneyldrm/exercises-dataset/blob/main//main/setup.html) | Contains `FRAMEWORK_META`, `DB_SQL`, `API_TEMPLATES`, and `buildLlmPrompt()` |
| **exercises.json** | [`/data/exercises.json`](https://github.com/hasaneyldrm/exercises-dataset/blob/main//data/exercises.json) | Source dataset (1,324 records) for API generation |
| **exercises.schema.json** | [`/data/exercises.schema.json`](https://github.com/hasaneyldrm/exercises-dataset/blob/main//data/exercises.schema.json) | Validation schema for generated backend models |
| **README.md** | [`/README.md`](https://github.com/hasaneyldrm/exercises-dataset/blob/main//README.md) | Usage documentation for the Ask Your LLM workflow |

## Summary

- **Six frameworks** are supported for LLM-generated backends: Express.js, FastAPI, ASP.NET Core, Spring Boot, Laravel, and Gin
- Framework metadata is centralized in [`setup.html`](https://github.com/hasaneyldrm/exercises-dataset/blob/main/setup.html) inside the `FRAMEWORK_META` object (lines 78-86)
- The `buildLlmPrompt()` function (lines 95-112) constructs framework-specific prompts automatically
- Generated prompts include package lists, run commands, and technical requirements tailored to each stack
- Database drivers adapt per framework to support PostgreSQL, MySQL, and SQLite

## Frequently Asked Questions

### Which backend framework produces the smallest Docker image for LLM-generated APIs?

**Gin (Go)** typically yields the smallest container images due to Go's static compilation and minimal runtime dependencies. A compiled Gin binary often stays under 20 MB, compared to 100+ MB for Node.js or Python bases. However, Express.js and FastAPI images can be optimized using distroless or Alpine bases if Go isn't in your stack.

### Can I modify the framework metadata to add unsupported frameworks?

Yes. Edit the `FRAMEWORK_META` object in [`setup.html`](https://github.com/hasaneyldrm/exercises-dataset/blob/main/setup.html) following the existing structure: provide `name`, `lang`, `pkg`, and `run` properties. You must also extend `API_TEMPLATES` with endpoint patterns matching your new framework's conventions. The prompt builder will automatically incorporate your additions.

### Does the LLM generate identical API structures across all frameworks?

The **endpoint semantics remain identical** (same URL patterns, response shapes, and query parameters), but implementation details follow each framework's idioms. Express.js uses middleware chains; FastAPI leverages Pydantic models and async handlers; Spring Boot generates annotation-driven controllers. The `API_TEMPLATES` object in [`setup.html`](https://github.com/hasaneyldrm/exercises-dataset/blob/main/setup.html) encodes these structural variations.

### Which database engines work with each backend framework?

**All six frameworks support PostgreSQL, MySQL, and SQLite**. The `DB_SQL` object in [`setup.html`](https://github.com/hasaneyldrm/exercises-dataset/blob/main/setup.html) generates dialect-specific schema definitions, and `FRAMEWORK_META.pkg` includes the appropriate driver packages for each combination. Switching databases requires only changing the `dbKey` parameter in `buildLlmPrompt()`—no manual code changes needed.