11. Next Steps

What you learned

• Define a schema as a Raku Grammar with named tokens

• Use LLM::Functions to normalize free-form text to your DSL

• Parse the DSL with Grammar + Actions

• Get typed, validated structured output

• Build schema-driven pipelines instead of fragile regex or prompt hacks

Things to explore next

Nested schemas

Add a token-address subrule with its own sub-tokens, and a nested class inside Booking. The Actionable role composes naturally.

Tool calling

Wire extract-booking as a tool in an LLM::Functions agent so the LLM can invoke it mid-conversation.

Streaming structured output

Parse incrementally as the LLM streams tokens — useful when the DSL is long.

Retries on validation failure

Catch a failed parse, feed the raw LLM output back with an error message, and ask it to correct the DSL. Usually one retry is enough.

Integration with agent workflows

Use the structured Booking / Ticket objects as typed messages passed between agents in a multi-step pipeline.

The Slangify Playground

Paste any Grammar + Actions pair into https://slangify.org to test and share your DSL interactively — no local install needed.

Further reading

This tutorial

• Slangify::Tutorial source code

• Slangify Website Examples

• Grammar Editor

Raku docs

• Raku Grammar tutorial

• Raku Regexes

• Raku Pod6

Key modules

• LLM::Functions on raku.land

• Actionable on raku.land

• JSON::Fast on raku.land