I’ve been building APIs for years, and something kept bothering me. I’d often find issues only at runtime—a field returning null when it shouldn’t, a client requesting data that didn’t exist. This led me to explore a better way. I wanted the confidence that my data structures were solid from the first line of code to the final API response. This is why I turned to creating type-safe GraphQL APIs. The combination of NestJS, Prisma, and a code-first approach gave me that safety net. It ensures what you define in your code is exactly what your API delivers, catching errors early. Let’s build something that feels robust together.

Think of this setup as a connected system. We start with our database model. Prisma reads a schema file and generates a fully typed client. This means every database query you write in your NestJS service has TypeScript checking it. No more guessing about column names or types.

How does this type safety actually flow through your app? It starts at the data layer.

First, we define our data shape with Prisma. After setting up your project, you create a schema.prisma file. This isn’t just configuration; it’s the source of truth. From this, Prisma creates types for your models.

// A simplified Book model
model Book {
  id          String   @id @default(cuid())
  title       String
  price       Decimal
  authorId    String
  author      Author   @relation(fields: [authorId], references: [id])
}

Running npx prisma generate creates a TypeScript client. Now, when you fetch a book, you know exactly what fields you’re getting.

Next, we connect this to GraphQL. With NestJS’s code-first approach, you don’t write GraphQL Schema Language (SDL) by hand. Instead, you use TypeScript classes with decorators. The framework builds the schema for you. This is where the magic links up.

You create a class that looks like a GraphQL type. The @ObjectType() decorator tells NestJS this is part of your GraphQL schema. The fields use @Field() decorators.

import { ObjectType, Field, ID, Float } from '@nestjs/graphql';

@ObjectType()
export class Book {
  @Field(() => ID)
  id: string;

  @Field()
  title: string;

  @Field(() => Float)
  price: number;

  @Field()
  authorId: string;
}

But wait, isn’t this just duplicating the Prisma model? Not quite. This Book class defines what your GraphQL API exposes. Your Prisma model defines what’s in the database. They can be different. You might not want to expose every database field.

The real connection happens in your service layer. This is your business logic. You use the typed Prisma client here.

import { Injectable } from '@nestjs/common';
import { PrismaService } from './prisma.service';
import { Book } from './book.model';

@Injectable()
export class BookService {
  constructor(private prisma: PrismaService) {}

  async findOne(id: string): Promise<Book> {
    // `book` is fully typed based on your Prisma schema
    const book = await this.prisma.book.findUnique({
      where: { id },
    });

    if (!book) {
      throw new Error('Book not found');
    }

    // TypeScript ensures the returned object matches the GraphQL `Book` type
    return book;
  }
}

Do you see the chain? Prisma gives you a typed database result. You then return it, and TypeScript checks it matches your GraphQL Book return type. A mismatch causes a compile-time error, not a runtime surprise for your API user.

What about relationships, like fetching a book with its author? This is a common GraphQL strength. You define a resolver for a specific field. The parent object (the book) might only have an authorId. The resolver’s job is to fetch the full author data.

import { Parent, ResolveField, Resolver } from '@nestjs/graphql';
import { Book } from './book.model';
import { Author } from './author.model';
import { BookService } from './book.service';

@Resolver(() => Book)
export class BookResolver {
  constructor(private bookService: BookService) {}

  @ResolveField(() => Author)
  async author(@Parent() book: Book): Promise<Author> {
    // The `book` parameter here has the Book type, including authorId
    return this.bookService.findAuthorForBook(book.authorId);
  }
}

This structure keeps your code organized and type-checked at every step. The @Parent() decorator gives you the book object. Your service method uses the typed Prisma client to find the author. The return type is the GraphQL Author object. The entire path is guarded by TypeScript.

Let’s talk about a common performance trap: the N+1 query problem. If you fetch a list of books and then their authors, a naive approach makes one query for the books and then one more query per book for the author. For 10 books, that’s 11 queries. Not good.

The solution is a pattern called DataLoader. It batches multiple requests for the same type of data into a single query. Setting it up adds another layer of type-safe efficiency.

You create a DataLoader for your authors. When multiple book resolvers ask for different authors, DataLoader collects all the author IDs and fetches them in one go.

What does this mean for you? Your API stays fast even with complex nested queries, and the implementation remains clean and type-safe. The DataLoader instance is typed, so you know what it returns.

Validation and error handling are also crucial. With this setup, input validation becomes straightforward. You can use the same class-based approach for your inputs.

import { InputType, Field } from '@nestjs/graphql';
import { IsString, IsNumber, Min } from 'class-validator';

@InputType()
export class CreateBookInput {
  @Field()
  @IsString()
  title: string;

  @Field()
  @IsNumber()
  @Min(0)
  price: number;
}

When this CreateBookInput is used in a mutation, the class-validator decorators run automatically if you set up the right pipe in NestJS. Invalid data never reaches your service logic. The error response is automatically formatted for GraphQL.

This whole process might seem like more setup initially. But the payoff is immense. You spend less time debugging and more time building features. Your development experience improves because your IDE can suggest fields and catch mistakes as you type. Your API consumers get clear, predictable, and well-documented endpoints.

The final piece is bringing it to life. Running the app starts a GraphQL playground. Here, you can test your queries and mutations with full auto-completion. The generated schema is always in sync with your code because your code is the schema. No more manual updates that get out of date.

Building an API this way changed how I work. It gives me a quiet confidence. The system guides me toward correct code. The types are a living document and a strict guardian. I encourage you to try this setup. Start with a single model and watch how the types connect from your database to your API endpoint.

Did this guide help you see the path to a more robust API? What part of this type-safe chain excites you the most? If you found this walkthrough useful, please share it with a colleague who might be battling runtime API errors. Let me know in the comments what you’re building with these tools