02 - Prompt Engineering: Reasoning, Structured Outputs & SDD
Estimated time to read: 5 minutes
Welcome to the Prompt Engineering & Structured Outputs module. This module bridges high-level semantic steering and deterministic software engineering: exploring cognitive prompting patterns, schema enforcement via Pydantic v2, and contract-first Spec-Driven Development (SDD).
🧭 Module Roadmap & Laboratories
To keep the material structured, rigorous, and production-oriented, this module is divided into four comprehensive laboratories:
1. 🟢 LAB-05: Prompt Reasoning Patterns: Few-Shot, CoT, Self-Consistency & ReAct
Implements and benchmarks cognitive reasoning induction: Zero-Shot, Few-Shot In-Context Learning (dynamic selection), Chain-of-Thought, Self-Consistency with Shannon Entropy consensus, and autonomous ReAct agent loops.
- Core concepts: In-Context Learning, Zero-Shot CoT, Majority Voting, Shannon Entropy, Tool Execution loops.
- Interactive Playground: reasoning_playground.ipynb
2. 🟢 LAB-06: Structured Outputs: Pydantic v2, Instructor & Schema Enforcement
Pipeline industrial de extração de dados estruturados com validação de esquema em tempo de execução via Pydantic v2, OpenAI Tool Calling/JSON mode, XML tag prompting e loops de auto-correção (Self-Healing).
- Core concepts: Pydantic v2 validation, JSON Schema, XML tag delimiters, Self-Healing reflection loops.
- Interactive Playground: structured_outputs_playground.ipynb
3. ⏳ LAB-07: Production Application: Financial Asset & Proposal Extractor (Em Desenvolvimento)
Aplicação de nível de produção para extração e análise de balanços corporativos e propostas de crédito bancário com validação contábil determinística (Ativo == Passivo + PL).
- Status: Planejado / Aguardando implementação da Issue #22.
4. ⏳ LAB-08: Spec-Driven Development: OpenSpec & Contract Compliance (Em Desenvolvimento)
Engenharia de IA baseada em contratos: especificações declarativas formais (.spec.yaml), geração automatizada de modelos Pydantic e auditoria de conformidade em runtime via SpecComplianceChecker.
- Status: Planejado / Aguardando implementação da Issue #23.
🛠️ Resources & References
Below are the academic references and technical guides used in this module, styled in our standardized post-card format:
📄 Academic & Seminal Papers
Language Models are Few-Shot Learners
The seminal GPT-3 paper introducing In-Context Learning (ICL) as an emergent capability of autoregressive models without weight updating.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Demonstrates that generating a series of intermediate reasoning steps dramatically improves the ability of large models to perform complex arithmetic, commonsense, and symbolic reasoning.
Large Language Models are Zero-Shot Reasoners
Introduces the classic trigger phrase 'Let\'s think step by step', unlocking multi-step deduction without needing task-specific few-shot exemplars.
Self-Consistency Improves Chain of Thought Reasoning in Language Models
Proposes sampling a diverse set of reasoning paths via non-zero temperature and marginalizing out reasoning paths to find the consensus answer via majority vote.
ReAct: Synergizing Reasoning and Acting in Language Models
Combines reasoning traces and task-specific actions in an interleaved loop, allowing LLMs to interact with external environments and tools dynamically.
📖 Technical References & Guides
Pydantic v2 Documentation
High-performance data validation and parsing using Python type hints, powered by the Rust-based pydantic-core engine.
Instructor: Structured LLM Outputs
Python library built on Pydantic to enforce typed JSON outputs from LLMs with automatic retries and validation hooks.
OpenSpec: Contract-First Specification Standard
Standardized declarative framework for defining AI agent behaviors, contracts, and evaluation benchmarks prior to implementation.