Slangify-Tutorial

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00. Slangify::Tutorial

Using DSLs with LLMs: From Prompt to Structured Output

Suppose you want an LLM to extract structured information from messy text — but you don’t want to hand-write JSON schemas or fragile parsing logic.

Input:

Jane booked a table for 4 at 7:30pm tomorrow at Bistro Verde

Desired output:

{
  "name": "Jane",
  "party_size": 4,
  "time": "7:30 PM",
  "restaurant": "Bistro Verde",
  "date": "tomorrow"
}

Slangify lets you define that structure with a DSL, then use an LLM to populate it reliably. This tutorial walks through everything from a first schema to a real-world pipeline.

What you will learn

• Understand what Slangify does

• Define a simple DSL schema using a Raku Grammar

• Connect it to an LLM via LLM::Functions

• Generate structured outputs from prompts

• Validate and post-process results

• See how this fits into a real workflow

Chapters

• 01. Setup

• 02. Your First Schema

• 03. Connecting to an LLM

• 04. The Generated Output

• 05. Iteration

• 06. Validation and Constraints

• 07. Prompt Engineering Patterns

• 08. Prompts

• 09. A Real-World Example

• 10. Common Pitfalls

• 11. Next Steps

Prompt Guide

Naturally, the example DSL code shown in this tutorial was itself generated by an LLM Agent (Claude Code). See LINK HERE for the prompts and tweaks we used to get a clean solution - adapt those for your own project.

More Info

Visit https://slangify.org for more information, examples and guidance.

Please ping the https://raku.org/community