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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>
155 lines
4.3 KiB
Markdown
155 lines
4.3 KiB
Markdown
ResearchPrompt
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*Research a topic...*
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# InfoTechPrimer – ResearchPrompt
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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**.
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> **Purpose:**
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> Produce a *factually grounded, scope-aware, source-anchored research brief* suitable as the direct input for authoring an InfoTechPrimer.
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It is designed to work well with:
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* General-purpose LLMs
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* Research-oriented agents
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* Human-in-the-loop review
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The prompt is **topic-agnostic**, but forces rigor, boundaries, and source grounding.
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---
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## ResearchPrompt
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```
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You are conducting foundational research for an InfoTechPrimer.
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Topic: <$topic>
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The goal is NOT to teach or promote, but to establish a precise, shared understanding
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of the topic for experienced IT professionals and AI systems.
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Produce a structured research brief that answers the following sections.
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Be concise, factual, and source-driven.
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Avoid tutorials, opinions, and vendor marketing language.
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---
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1. Canonical Definition
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- Provide the most widely accepted definition(s) of the topic.
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- If multiple definitions exist, explain why and in which contexts they differ.
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- Prefer definitions from standards bodies, original designers, or official specifications.
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---
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2. Domain Context and Classification
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- Which technical domain(s) does this topic belong to?
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(e.g. systems programming, distributed systems, security, AI, quantum computing)
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- What *type* of thing is it?
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(e.g. protocol, framework, architectural style, API standard, SDK, language, library)
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- At which abstraction level does it primarily operate?
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---
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3. Historical Origin and Motivation
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- Who introduced it and when?
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- What concrete problem(s) was it created to solve?
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- What existing approaches did it replace, extend, or formalize?
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(Only include history that explains intent or constraints.)
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---
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4. Core Concepts and Invariants
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- List the essential concepts without which the topic would not make sense.
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- For each concept, explain its role in one or two sentences.
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- Identify any invariants, guarantees, or formal assumptions.
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---
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5. Scope Boundaries
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- Clearly state what the topic explicitly covers.
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- Clearly state what it explicitly does NOT cover.
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- Identify common misconceptions or misuses.
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This section should prevent overextension by AI systems.
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---
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6. Practical Implications (Non-Tutorial)
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- What design or architectural consequences follow from using this?
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- What tradeoffs are inherent?
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- What kinds of systems typically depend on it?
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Do NOT include step-by-step usage.
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---
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7. Relationship to Adjacent Concepts
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- List closely related standards, technologies, or terms.
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- For each, explain the relationship (complementary, layered on top, alternative, predecessor).
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---
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8. Authoritative Sources
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- List primary, authoritative references:
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- Standards (RFCs, ISO, W3C, IEEE, etc.)
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- Official specifications or documentation
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- Foundational papers
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- Include direct links.
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- Clearly distinguish primary sources from secondary explanations.
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---
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9. Stability and Maturity Assessment
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- Is this topic considered stable, evolving, or experimental?
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- Are there competing standards or dominant implementations?
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- Is backward compatibility a concern?
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---
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10. Notes for Primer Authoring
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- Highlight points that MUST be stated clearly in a primer.
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- Highlight areas where ambiguity must be avoided.
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- Identify terminology that must be used consistently.
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---
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Constraints:
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- Use precise, declarative language.
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- No metaphors or analogies.
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- No marketing or opinionated statements.
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- Assume a technically literate audience.
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- Prefer explicit statements over implied assumptions.
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```
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---
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## Why This Prompt Works Well
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This prompt is intentionally shaped to:
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* **Force scope clarity** (critical for AI agents)
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* **Surface invariants and constraints**
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* **Separate definition from implementation**
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* **Anchor everything in primary sources**
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* **Produce output that maps 1:1 to your Primer Authoring Rules**
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You can think of it as:
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> *A pre-primer that de-risks the primer.*
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---
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## How You’ll Likely Use It in Practice
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Typical flow:
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1. Run this prompt on a topic (e.g. CUDA, OAuth 2.0, OpenQASM)
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2. Review and correct factual issues
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3. Collapse the research brief into the Primer schema
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4. Apply brand tone + structural rules
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5. Publish as an InfoTechPrimer
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xxx
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