Structured Outputs & Parsing
Moving from free-form text to reliable, machine-readable data pipelines
Session Topics
JSON Schema Design
Designing typed schemas that constrain LLM output to structured data — the foundation for any AI pipeline that feeds downstream systems.
Output Formatting Techniques
Prompting for JSON, Markdown, XML, and custom formats. Format teaching via examples and schema constraints.
Error Handling & Validation
What to do when LLMs produce malformed output: retry strategies, partial parsing, and graceful degradation.
Pipeline Construction
Chaining LLM calls with parsing, validation, and transformation steps to build robust AI data pipelines.
Real-World Applications
How structured outputs power enterprise AI: legal document parsing, resume extraction, meeting summarisation, and data enrichment.
Build Your Resume Data Pipeline
Take your production-ready prompt from Session 3. Transform it to generate structured JSON representing a student resume. Validate the output, extract specific fields, and build a simple pipeline that processes this structured data programmatically.
A prompt set for structured resume parsing tested on 3 different inputs, showing where it succeeds or fails.
Key Takeaways
- Unstructured LLM output cannot be reliably integrated into real systems
- JSON Schema is the most practical constraint mechanism for production use
- Always validate and handle malformed output — it will happen
- Structured output is what separates a demo from a deployable product