Prompt Engineering by Ermetica7: Ethical, Modular

Our Competence

We design prompts that guide large language models with precision. Our method integrates:

Modular Design: Persona, task, and output format are separated and recombined for clarity and reuse.
Constraint Engineering: We use negative instructions to prevent generic or biased outputs.
Multimodal Integration: We build prompts that combine text with images, audio, or structured data.
Role Simulation: We assign expert personas to shape tone, vocabulary, and domain knowledge.
Few-Shot & Chain-of-Thought: We guide reasoning through examples and step-by-step logic.
Structured Output: We specify formats, JSON, Markdown, tables, for predictable, machine-readable results.
External Context (RAG): We ground prompts in real documents, reports, and datasets to eliminate hallucination.

Our Philosophy

We believe prompts are semantic contracts, not commands. They are blueprints for interpretive behavior. Our work is rooted in clarity, autonomy, and ethical design. We reject automation for its own sake. We build systems that think.

“Architect the meaning first. Let the model follow.”

What’s Possible

Our prompt engineering services are used to:

Simulate expert analysis (e.g. legal, financial, scientific)
Generate multilingual SEO content with entity alignment
Design AI workflows for structured content generation
Build RAG pipelines for grounded, document-aware responses
Train internal teams on prompt strategy and semantic control

Semantic SEO & E-E-A-T

Every prompt we design is semantically structured. We align with schema.org, optimize for entity recognition, and embed editorial judgment. Our work reinforces E-E-A-T: expertise, experience, authority, and trust. We don’t chase keywords, we build meaning.

Our prompts are used in Retrieval-Augmented Generation (RAG) systems, multilingual SEO, and AI content governance. We design for crawlability, clarity, and strategic discoverability.

Work With Us

We offer prompt engineering as a strategic service, custom, ethical and grounded in interpretive clarity. Whether you need AI to simulate expert analysis, generate structured outputs, or align with multilingual SEO, we design the blueprint behind the response.

Contact Anna and Andrea to begin.

Ermetica7 Philosophy of Prompt Engineering

Philosophically, we are a bridge between human intent and artificial intelligence’s potential. We operate on the principle that true control over a powerful, non-deterministic system isn’t found in a simple command, but in shaping the environment and context in which it operates.

Think of it as the difference between giving a single word to a poet and giving them a full, structured outline for an epic poem. We don’t command the AI; we architect the request. We define a problem so clearly that the solution becomes almost inevitable.

Our work is less about what the AI can do and more about what the human can guide the AI to do. We address the philosophical problem of the “black box” by allowing you to define the AI’s persona ( P), its knowledge base ( E), and its reasoning process ( E). We are not asking a question—we are constructing a world for the AI to inhabit, complete with rules ( U), roles, and a specified form for its creations ( S).

An Algebraic Explanation

Algebraically, we describe a large language model (LLM) as a function:

o = L(p, S)

Where p is the prompt, S is the model’s internal state and knowledge, and o is the output. Our approach doesn’t alter L or S . Instead, we construct a far more structured and intentional input, P , using modular design.

Modularity (M): The input P is composed of distinct components:
P = ModulePersona + ModuleTask + ModuleOutput + …
This is a structured concatenation of strings and semantic data.
Persona (P) & Unconstrained (U): We define roles and apply negative constraints:
P = Ppersona + Ptask + Poutput + … + Cnegative
This shapes tone, behavior, and boundaries.
Few-Shot / Chain-of-Thought (E): We embed examples and reasoning steps:
P = Pbase + ∑(pi, oi)
This teaches the model how to think before it speaks.
Structured Output (S): We constrain the format of o :
o′ = fschema(o)
This ensures the output conforms to a predefined schema, machine-readable and predictable.

In summary, we engineer the input p to be as rich, specific, and structured as possible, thereby increasing the predictability and quality of the output o . The function L remains unchanged; the elegance lies in the construction of its input.

Ermetica7 Ethical Manifesto

We are Anna and Andrea, co-founders of Ermetica7. Our work is not built on automation, it’s built on interpretation. We believe that ethical design begins with clarity, and that clarity is a moral act. Every prompt we craft, every system we architect, is a reflection of our responsibility to language, logic, and the humans who engage with AI.

We reject opacity. We reject manipulation. We reject the idea that machines should be optimized for engagement at the cost of truth. Our Purpose is interpretive clarity, systems that think, not just perform.

Our Commitments

Transparency: We disclose how prompts are structured, how outputs are shaped, and how context is sourced.
Bias Awareness: We design prompts that mitigate bias, not amplify it. We audit tone, role, and reasoning.
Factual Integrity: We ground AI responses in real data using Retrieval-Augmented Generation (RAG). No hallucination, no guesswork.
Human-Centered Design: We prioritize accessibility, clarity, and autonomy. Our systems are built for understanding, not persuasion.
Editorial Judgment: We apply semantic rigor and ethical filters to every output. We don’t chase virality—we build meaning.

Why It Matters

AI is not neutral. Every prompt is a lens. Every output is a signal. Our ethical stance is embedded in the architecture of our work. We don’t just design for performance, we design for accountability.

“Precision is ethical. Clarity is moral. Interpretation is responsibility.”

This manifesto is not a policy, it’s a practice. It informs how we build, how we collaborate, and how we evolve. If you work with Ermetica7, you’re not buying a service. You’re joining a philosophy.

Frequently Asked Questions

What is prompt engineering at Ermetica7?

For us, prompt engineering is not technical, it’s interpretive. We design prompts as semantic systems: modular, ethical, and strategically grounded. Each prompt is a structured input that guides AI behavior with clarity and control. Learn more in our Prompt Toolkit.

How do you structure a prompt?

We use modular design: persona, task, constraints, and output format are separated and recombined. This allows us to build prompts algebraically, as outlined in our AI Content Catalyst. We don’t write commands, we architect semantic contracts.

What makes your approach ethical?

We embed editorial judgment, bias mitigation, and factual grounding into every prompt. Our Ethical Manifesto outlines our commitment to transparency, human-centered design, and Retrieval-Augmented Generation (RAG) to eliminate hallucination.

Can you integrate external documents into a prompt?

Yes. We use RAG workflows to ground the AI in real data, reports, transcripts, datasets, so it responds based on your source material. Explore how we structure this in Semantic Structuring.

Do you support multilingual prompt design?

Absolutely. We design prompts that align entities and intent across languages, ensuring semantic consistency and SEO discoverability. Read our guide on International SEO Prompts.

What kind of outputs can you structure?

We specify formats like JSON, Markdown, tables, or bullet lists, making AI outputs predictable, machine-readable, and ready for integration. See examples in our Content Cluster Playbook.

How do you guide AI reasoning?

We use Few-Shot examples and Chain-of-Thought logic to teach the model how to think before it speaks. This reduces errors and improves transparency. Learn how we apply this in Semantic AI Strategy.

Who are Anna & Andrea?

We are the co-founders of Ermetica7. Our background blends editorial strategy, semantic SEO, and AI systems design. We build prompts that interpret, not just automate. Meet us on the About Ermetica7 page.