Context engineering shifts RAG focus from prompt tuning to structured data assembly for LLM calls. The single-document architecture utilizes four bricks—parsing, question parsing, retrieval, and generation—to produce typed context pieces. These include system prompts, filtered document segments, and structured metadata. This engineering discipline improves auditability, enables caching, and supports scalable component composition.
- Four-brick pipeline: parsing, question parsing, retrieval, generation
- Typed data outputs for LLM context assembly
- Fixed system prompts for caching efficiency
- Filtered document lines and structured metadata
- Improved auditability and cost control
- Four-brick pipeline: parsing, question parsing, retrieval, generation
- Typed data outputs for LLM context assembly
- Fixed system prompts for caching efficiency
- Filtered document lines and structured metadata
- Improved auditability and cost control
This tutorial provides a comprehensive guide on using Google's LangExtract library to transform unstructured text into machine-readable structured data. By leveraging OpenAI models, the guide demonstrates how to build reusable extraction pipelines for various document types such as legal contracts, meeting notes, and product announcements. The workflow includes setting up dependencies, designing precise prompts with example annotations for grounding, and implementing interactive visualizations of extracted entities.
Key topics covered:
- Implementing structured data extraction using LangExtract and OpenAI
- Designing prompt templates and providing few-shot examples for entity recognition
- Building specialized pipelines for contract risk analysis and meeting action item tracking
- Handling long-document intelligence and batch processing workflows
- Visualizing extracted information through HTML and organizing results into tabular datasets via Pandas
Key topics covered:
- Implementing structured data extraction using LangExtract and OpenAI
- Designing prompt templates and providing few-shot examples for entity recognition
- Building specialized pipelines for contract risk analysis and meeting action item tracking
- Handling long-document intelligence and batch processing workflows
- Visualizing extracted information through HTML and organizing results into tabular datasets via Pandas
