Workflow · Production
Guided generation techniques for constraining LLM outputs to specific formats
The problem
LLMs like GPT-4 and Gemini Pro generate useful text but without guidance their outputs do not reliably adhere to specific formats or structures needed for real-world integration.
Workflow diagram · grounded in source
1
Initialize Vertex AI client
trigger
“We will run this code using Vertex AI. First, let's initialize our environment and set up the Vertex AI client with the necessary configuration to ensure our outputs are both useful and safe”
2
Regex-guided generation
ai_action
“Regular expressions (regex) are a powerful way to ensure that generated text matches a specific pattern. E.g. Imagine you need a 6-digit number. By defining a regex pattern, you can validate the generated number, ensuring it's exactly si…”
3
JSON schema validation
validation
“By validating the generated JSON against a schema, you ensure that the output adheres to the expected structure and data types. This technique is useful for applications requiring precise and predictable data formats.”
4
CFG-structured generation
ai_action
“Context-Free grammars (CFGs) allow us to define a set of production rules for generating structured sentences. CGFs are excellent for generating structured sentences or text that follows a specific set of grammatical rules. This method i…”
5
Template-based text output
output
“Template-based generation uses predefined templates to structure the generated text. E.g., you can create a user profile using a template that specifies placeholders for the name, age, and email. This method ensures that the generated co…”
6
Structured data generation
output
“Structured data generation involves creating data in a tabular format, such as CSV, which can be easily converted into a DataFrame for analysis or processing.”
Reported outcome
Guided generation techniques — regex, JSON schemas, CFGs, templates, entities, and structured data generation — improve the accuracy and reliability of LLM outputs and make it easier to integrate LLMs into real-world applications.
Reported stack
Vertex AIGPT-4Gemini ProJupyter NotebookGCP
Frequently asked questions
What did this team achieve with this AI workflow?
Guided generation techniques — regex, JSON schemas, CFGs, templates, entities, and structured data generation — improve the accuracy and reliability of LLM outputs and make it easier to integrate LLMs into real-world…
What tools did this team use?
Vertex AI, GPT-4, Gemini Pro, Jupyter Notebook, GCP.
How is this workflow AI workflow structured?
Initialize Vertex AI client → Regex-guided generation → JSON schema validation → CFG-structured generation → Template-based text output → Structured data generation.