Prompt Architecture

prompt architecture Link to heading

External validation: Passed the Nate B. Jones 5-Level Prompting assessment with a 5/5 rating covering all critical competencies from basic prompt construction to meta-prompting systems design.

Advanced prompt architecture Link to heading

Multi-pattern integration — Research Brief, Few-Shot, Action-Oriented, and Constitutional patterns combined in real-world scenarios, not toy examples.

Three-level specificity mastery

  • Task Specificity — precise deliverable definition
  • Context Specificity — comprehensive background framing
  • Output Specificity — detailed format and structure requirements

Context management & token efficiency Link to heading

  • Progressive disclosure — 4-step strategies for large document processing with adaptive chunking and user calibration
  • Personalization integration — CV-based filtering systems that customize AI interactions to user expertise gaps
  • Interactive optimization — feedback loops for real-time prompt refinement and validation

Meta-prompting & systems design Link to heading

  • Quality-assurance meta-prompts — pre-execution analysis with ambiguity detection, context validation, parameter management
  • Model optimization — frameworks for adapting prompts to specific model strengths (speed, reasoning, external search)
  • Anticipatory planning — systems that predict and prepare for follow-up questions and edge cases

Prompt-as-code Link to heading

The prompt library is versioned with code, not pasted into chat windows.

  • 377-line XML prompt schema (ideation-intent/prompt-schema.md) separating model-native capabilities (toolset, memory, provenance) from user-defined orchestration (abstract, role, guardrails)
  • 8 promptsets under prompts/ — Dark Code audit/context/comprehension, Proper Skills deployment, etc.
  • 173+ lines of interactive session logs documenting iterative prompting

Schema-first governance for agent round-trip Link to heading

The same discipline applies to ontology. Custom SHACL 1.2 Turtle vocabulary (mdu: namespace, sh:sparql constraints with $data substitution into DuckDB SQL) is designed specifically so an agent can round-trip the Turtle without losing dialect detail. From one .ttl file: Postgres / BigQuery / Databricks DDL plus Parquet / Iceberg (S3 + AWS Glue) / DuckLake / Nimtable lake schemas.

Key strengths Link to heading

  1. Systems thinking — prompting as workflow design, not tactical instruction improvement
  2. Pattern recognition — intuitive combination of multiple prompting patterns
  3. Efficiency optimization — comprehensiveness balanced against token economy
  4. Human-in-the-loop design — interactive, adaptive systems
  5. Knowledge architecture — frameworks for scalable skill transfer (e.g., the 5-section prompt-engineering curriculum)