What is Prompt Engineering?
Prompt Engineering is about precisely defining the role, objective, context, and format of a prompt so as to drive AI to the desired output. For RAIT88, a leader in integrating AI and VR solutions for defense, this discipline translates into faster technical manuals, more faithful training simulators, and decision-making processes supported by securely generated insights.
| Technique | What it does | Quick example |
|---|---|---|
| Role (Role) | Set the "voice" of the model | "Act as an SEO copywriter experienced in the defense" |
| Objective (Goal) | Defines the expected result | "Write an article about the engineering prompt" |
| Context (Context) | Provides data, constraints, and audience | "RAIT88 blog, B2B target, authoritative tone" |
| Format (Output Format) | Specifies output structure | "H-structure, meta tag, final CTA" |
| Iteration (Refine & Retry) | Provides for testing and improvement cycles | "Version 0.1 → feedback → 0.2, 0.3..." |
Practical note: these five steps are exactly what we applied to generate this article.
Prompt engineering techniques: the making-of of this article (in-depth)
Initial prompt
"Act as an experienced SEO copywriter. You need to write an article on prompt engineering for the RAIT88 blog (defense sector, B2B audience). The article must contain meta tags, H-structure, CTAs and explain the main techniques by showing the process of creating the article itself."
Details added after the brief:
- Include the keyword in the first sentence of the introduction.
- Avoid bullet points in the introduction and first paragraph.
- Expand section 3 with mind map and explanation of each step.
Chain-of-Thought and mind map
├── Meta SEO.
│ ├── Slug
│ ├─── Title
│ └── Description
├─── Introduction (keyword first sentence)
├─── Section 1 - Definition
├─── Section 2 - Fundamental techniques.
│ ├─── Role
│ ├── Goal
│ ├── Context
│ ├── Format
│ └─── Iterate
├─── Section 3 - Making-of
│ ├─── Prompt v0.0
│ ├─── CoT & Mental Map
│ ├─── Self-Consistency
│ ├─── Reflection pass
│ └─── Refine & Retry
└─── CTA finalEach node was then expanded into paragraphs, ensuring that keyword density remained ~1 % and that H2 titles contained related terms ("prompt engineering techniques," "making-of prompt engineering").
Self-Consistency - controlled variant generation.
Three parallel drafts (A, B, C) were produced with minor variations in style and section order. Metrics applied:
- Readability (Italian Flesch): > 55
- Keyword density ("prompt engineering"): 0.9 - 1.1 %.
- Token cost: < 5 k per iteration Draft B received the highest score and became the basis for version 0.1.
Reflection pass - quality and security check.
Control Prompt sent to the model:
"Check that the meta description does not exceed 155 characters, that there is no classified information, and that the main keyword appears in the first 100 characters of the article."
The template confirmed compliance and suggested moving the adjective "crucial" next to the keyword for greater SEO impact.
Refine & Retry - incorporation of user feedback.
Feedback received: introduce an opening paragraph without bullet points and expand section 3 with more details. Actions:
- Removed the bulleted list from the introduction and rewrote in discursive form.
- Expanded the mind map with sub-nodes and quantitative explanations (keyword density, readability).
- Updated the versioning to 0.2, ready for further comments.
Best practices in Prompt Engineering.
- Precision Before Poetry: the more detailed the prompt, the less revisions will be needed.
- Split & Conquer: split meta tags, body and CTAs into micro-prompts for granular control.
- Documented Versioning: saves every iteration; facilitates audits and rollbacks.
- Security Layer: always integrate an audit pass on data sensitivity.
- Clear KPIs: drafting time, number of revisions, token cost, and readability metrics.
