# makersuite

Published articles for makersuite.

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## A Developer's Journey To the AI and GraphQL Galaxy

DevFeed: [A Developer's Journey To the AI and GraphQL Galaxy](<https://devfeed.tech/articles/a-developer-s-journey-to-the-ai-and-graphql-galaxy-26123.md>)

Original publisher: [Read original article](<https://medium.com/@yonatanvlevin/a-developers-journey-to-the-ai-and-graphql-galaxy-3e8e7fd41928?source=rss-8735065c2497------2>)

Author: Yonatan (Yoni) Levin

Published: 2023-10-03T03:50:29Z

Content type: opinion

Language: en

Sources: [Stories by Yonatan V. Levin on Medium](<https://devfeed.tech/sources/stories-by-yonatan-v-levin-on-medium.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [GraphQL](<https://devfeed.tech/topics/graphql.md>), [API](<https://devfeed.tech/topics/api.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [graphql](<https://devfeed.tech/tags/graphql.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [llm](<https://devfeed.tech/tags/llm.md>), [makersuite](<https://devfeed.tech/tags/makersuite.md>), [prompt-engineering](<https://devfeed.tech/tags/prompt-engineering.md>), [vector-database](<https://devfeed.tech/tags/vector-database.md>)

### AI overview

The article describes a personal exploration of using generative AI and large language models to generate GraphQL queries and mutations for the monday.com API. It explains the challenge of producing valid API-specific GraphQL and discusses an Implementation Consultant concept that creates tailored boards, while noting that the explored solution is not production-ready.

### Source excerpt

It's all starts with simple drawingsThe article that helps tame an LLM to produce a GraphQL query for a specific API Generative AI has stirred debates. While some might regard it as a fleeting trend, I view it as a game-changer. AI and Large Language Models (LLM) combined give way to AI-powered chatbots that hold great potential for improving operations and customer services. Such AI bots are designed to communicate with an entire system via an API. Sounds okay... unless your API is built using GraphQL. What's the trick, you ask? GraphQL stands out as a potent query language due to its flexibility, versatility, and extensibility. Getting LLM to produce a perfect and valid GraphQL query for a specific API is challenging. Some even say it's impossible... At monday.com, innovation is our mantra. We set off on the AI journey some time ago, and very soon I realized that mastering AI feels like discovering a new galaxy far, far away -- there are no ready answers, so you need to trust you gut feeling and push the boundaries. And this thrill of making the impossible possible makes my determination surge like the Force within a Jedi. We started at monday.com with foundational tasks such as "Summary Update" and "Generate tasks". These tasks, powered by basic prompts, enabled app developers to create first bespoke AI solutions within the monday.com framework. https://medium.com/media/5f9f975664482bc1fa9a03a95b7481bf/hrefhttps://medium.com/media/2236f6215c3b2977349c18c538b95ca1/href Our ambition grew when we decided to create the Implementation Consultant. Think of it as a "droid" that estimates monday.com users' needs and offers bespoke boards to them. Once a user gives the go-ahead, this "droid" seamlessly crafts a GraphQL mutation over the monday.com API and creates the individually tailored solution. https://medium.com/media/36cb4871ab825aaf89514e37628b4c18/href However, using our LLM to generate these GraphQL API calls was like navigating the asteroid field -- so tricky! Our qui