tegwick
360c3b1de2
Some checks failed
Test Suite / unit-tests (3.11) (push) Has been cancelled
Test Suite / unit-tests (3.12) (push) Has been cancelled
Test Suite / integration-tests (push) Has been cancelled
Test Suite / e2e-tests (push) Has been cancelled
Test Suite / performance-tests (push) Has been cancelled
Test Suite / code-quality (push) Has been cancelled
Test Suite / security-scan (push) Has been cancelled
Test Suite / test-summary (push) Has been cancelled
This example demonstrates the full workflow of generating InfoTech primers
using MarkiTect's Prompt Dependency Resolution infrastructure.
Features demonstrated:
- Artifact creation and storage with content-based addressing
- PromptTemplate with @{macro} resolution across multiple spaces
- Automatic dependency tracking and graph construction
- Provenance tracing from outputs back to inputs
- Visualization export (Mermaid format)
- Incremental execution with change detection
Files added:
- generate_primers.py: Complete working example
- README.md: Quick start guide and architecture overview
- TUTORIAL.md: Comprehensive 500+ line tutorial
- templates/generate-primer.md: Template with macros
- artifacts/topics/: ETL and Microservices topic definitions
- artifacts/guidelines/: Authoring rules and research protocol
- prepdr/: Original manual system (preserved for reference)
Example output:
- Generates 2 primers (ETL, Microservices)
- Creates 8 artifacts across 4 information spaces
- Records 8 dependency edges in SQLite database
- Exports dependency graph visualization
Run with: cd examples/content-generator && python generate_primers.py
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
4.3 KiB
4.3 KiB
ResearchPrompt
Research a topic...
InfoTechPrimer – ResearchPrompt
Below is a single, reusable, high-precision research prompt you can use to systematically get a grip on any InfoTech topic before writing an InfoTechPrimer.
Purpose: Produce a factually grounded, scope-aware, source-anchored research brief suitable as the direct input for authoring an InfoTechPrimer.
It is designed to work well with:
- General-purpose LLMs
- Research-oriented agents
- Human-in-the-loop review
The prompt is topic-agnostic, but forces rigor, boundaries, and source grounding.
ResearchPrompt
You are conducting foundational research for an InfoTechPrimer.
Topic: <$topic>
The goal is NOT to teach or promote, but to establish a precise, shared understanding
of the topic for experienced IT professionals and AI systems.
Produce a structured research brief that answers the following sections.
Be concise, factual, and source-driven.
Avoid tutorials, opinions, and vendor marketing language.
---
1. Canonical Definition
- Provide the most widely accepted definition(s) of the topic.
- If multiple definitions exist, explain why and in which contexts they differ.
- Prefer definitions from standards bodies, original designers, or official specifications.
---
2. Domain Context and Classification
- Which technical domain(s) does this topic belong to?
(e.g. systems programming, distributed systems, security, AI, quantum computing)
- What *type* of thing is it?
(e.g. protocol, framework, architectural style, API standard, SDK, language, library)
- At which abstraction level does it primarily operate?
---
3. Historical Origin and Motivation
- Who introduced it and when?
- What concrete problem(s) was it created to solve?
- What existing approaches did it replace, extend, or formalize?
(Only include history that explains intent or constraints.)
---
4. Core Concepts and Invariants
- List the essential concepts without which the topic would not make sense.
- For each concept, explain its role in one or two sentences.
- Identify any invariants, guarantees, or formal assumptions.
---
5. Scope Boundaries
- Clearly state what the topic explicitly covers.
- Clearly state what it explicitly does NOT cover.
- Identify common misconceptions or misuses.
This section should prevent overextension by AI systems.
---
6. Practical Implications (Non-Tutorial)
- What design or architectural consequences follow from using this?
- What tradeoffs are inherent?
- What kinds of systems typically depend on it?
Do NOT include step-by-step usage.
---
7. Relationship to Adjacent Concepts
- List closely related standards, technologies, or terms.
- For each, explain the relationship (complementary, layered on top, alternative, predecessor).
---
8. Authoritative Sources
- List primary, authoritative references:
- Standards (RFCs, ISO, W3C, IEEE, etc.)
- Official specifications or documentation
- Foundational papers
- Include direct links.
- Clearly distinguish primary sources from secondary explanations.
---
9. Stability and Maturity Assessment
- Is this topic considered stable, evolving, or experimental?
- Are there competing standards or dominant implementations?
- Is backward compatibility a concern?
---
10. Notes for Primer Authoring
- Highlight points that MUST be stated clearly in a primer.
- Highlight areas where ambiguity must be avoided.
- Identify terminology that must be used consistently.
---
Constraints:
- Use precise, declarative language.
- No metaphors or analogies.
- No marketing or opinionated statements.
- Assume a technically literate audience.
- Prefer explicit statements over implied assumptions.
Why This Prompt Works Well
This prompt is intentionally shaped to:
- Force scope clarity (critical for AI agents)
- Surface invariants and constraints
- Separate definition from implementation
- Anchor everything in primary sources
- Produce output that maps 1:1 to your Primer Authoring Rules
You can think of it as:
A pre-primer that de-risks the primer.
How You’ll Likely Use It in Practice
Typical flow:
- Run this prompt on a topic (e.g. CUDA, OAuth 2.0, OpenQASM)
- Review and correct factual issues
- Collapse the research brief into the Primer schema
- Apply brand tone + structural rules
- Publish as an InfoTechPrimer
xxx