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GraphQL Best Practices

You're reading from   GraphQL Best Practices Gain hands-on experience with schema design, security, and error handling

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781835467145
Length 422 pages
Edition 1st Edition
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Author (1):
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Artur Czemiel Artur Czemiel
Author Profile Icon Artur Czemiel
Artur Czemiel
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Table of Contents (23) Chapters Close

Preface 1. Part 1 - Why GraphQL?
2. Chapter 1: Unveiling the Holy Grail of Communication – GraphQL FREE CHAPTER 3. Chapter 2: Applying an AI-Friendly Approach to GraphQL 4. Part 2 - Schema-First Design Patterns
5. Chapter 3: Crafting Effective GraphQL Schemas 6. Chapter 4: Building Pipes 7. Part 3 - Exploring Possible Ways to Use GraphQL
8. Chapter 5: Transitioning from REST to GraphQL 9. Chapter 6: Defining GraphQL Transformers 10. Chapter 7: Understanding GraphQL Federation 11. Part 4 - Advanced GraphQL
12. Chapter 8: Executing Schema-First Systems 13. Chapter 9: Working on the Frontend with GraphQL 14. Chapter 10: Keeping Data Secure 15. Chapter 11: Describing Errors in GraphQL 16. Chapter 12: Documenting your Schema 17. Chapter 13: Tackling Schemas with Visualization 18. Part 5 - From an Idea to a Working Project
19. Chapter 14: From an Idea to a Working Project – Backend Development with GraphQL and TypeScript 20. Chapter 15: From an Idea to a Working Project – Frontend Integration with GraphQL and TypeScript 21. Index 22. Other Books You May Enjoy

Writing GraphQL queries with AI

Indeed, writing GraphQL queries is not difficult for those familiar with the GraphQL language, and tools such as GraphiQL make it even easier. However, with a deep understanding of the schema and its structure, AI should be able to handle this task as well.

Instead of manually writing a query to receive books by Robert Ludlum, we could simply say, “Hey, give me a list of all books by Robert Ludlum available in the library.” Then, under the hood, AI can generate the appropriate query, and GraphQL can return the data accordingly. This demonstrates how AI can assist developers in composing complex queries by understanding natural language instructions.

Here, let’s create a message system that includes LLM variables. These variables are written in uppercase letters, and in our case, they are SCHEMA_HERE and PROMPT_HERE – this makes it easier to understand the prompt structure for our LLM system:

You are a query generator...
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