Frequently Asked Questions

What is the difference between Alegoria and the main AI solutions? (Notebook LM, Perplexity, GPT Pro, etc…) #

Alegoria differentiates itself from generic platforms like NotebookLM or ChatGPT Pro by being a SaaS solution specialized in transforming technical documents into playful narrative scripts for social media, with knowledge graphs for traceability and regulatory compliance. More than that: The Alegoria Methodology handles the translation of documentation into Narrative Journeys, structuring the strategy for presenting technical content by focusing on what is relevant and allowing the user to track and control the generated content.

It’s important to emphasize that Alegoria focuses on parameterized scripting for specific audiences (technicians, teachers, influencers), with multilingual support and optimized selection of AI models for technical narratives without loss of factual accuracy — something absent in generalist tools.

This way, with Alegoria, the user can create narrative scripts that compose journeys, maintain control over what was created, and optimize scripting work on an AI platform tailored for this niche.

Can I do what Alegoria does just using ChatGPT? After all, I’ve been scripting content with GPT for a while. #

No: using only ChatGPT (even with good prompts) does not deliver the same result as a platform like Alegoria, especially when the problem involves scale, traceability, and complex technical documents.

Limits of using only ChatGPT #

Scripting non-technical, short content focused on humor, drama, or romance can be done with quality using well-crafted prompts, adjusting tone, persona, and objective for each piece. However, this depends on intense manual work from the author, who needs to select facts, adapt the context, test variations, and review each script individually.

In practice, this runs into three bottlenecks:

• Scale: as the volume of scripts increases, manual review starts consuming hours of work and quality fluctuates based on fatigue and creative block.
• Consistency: maintaining coherence in tone, framing, and terminology across dozens or hundreds of scripts requires controls not built into isolated chatbot use.
• Structured sources: when the input is a set of standards, manuals, or extensive reports, it’s hard to ensure the author is always using the correct and complete version of the information.

Where Alegoria expands on what GPT does #

Alegoria starts from the premise that the problem is not “generating pretty text,” but transforming technical knowledge into reusable, auditable narrative journeys aligned with institutional objectives. Instead of relying solely on the scriptwriter’s prompt skills, the platform operates on three layers:

• Structured knowledge base Technical documents, standards, specifications, and manuals are organized as knowledge graphs (entities, relationships, facts) that can be reused across multiple scripts, reducing rework and risk of distortion.

• Orchestration of specialized agents Different AI agents act in specific stages: identifying relevant facts, adapting to the target audience, checking regulatory adherence, simplifying language, among others. This reduces dependence on the “magic” of a single prompt and increases result predictability.

• Journeys and production metrics Script production is organized into journeys parameterized by audience, duration, channel, and objective, allowing replication of effective formats, measuring results, and iterating systematically.

Indicators and scale arguments #

When comparing “just ChatGPT” with Alegoria, some indicators help materialize the difference:

• Production time per script

  • Manual flow with GPT: the author needs to open the document, select excerpts, adapt the prompt, review, adjust tone, and redo when necessary. In real scenarios, this typically consumes dozens of minutes per complex script.
  • Alegoria: most of this flow is automated from the knowledge base and agents; the technician acts more as a curator than as a “writer from scratch,” reducing time to minutes even for those without scripting experience.

• Reuse of technical knowledge

  • Manual flow: history gets lost in scattered conversations, with no guarantee of reusing previous framing, analogies, or explanations.
  • Alegoria: each script is linked to entities, facts, and decisions in a structured catalog, enabling creation of series, expansions, and thematic variations without rework.

• Governance and compliance

  • Manual flow: no native tracking of which standard, law, or manual backed a section; reviewing regulatory adherence depends on human line-by-line reading.
  • Alegoria: the graph architecture itself and the AI assistant support adherence verification, explainability, and traceability, which is critical in public, health, education, or regulated sectors.

When it makes sense to stick with just ChatGPT #

If the goal is:

• Producing one-off, creative pieces with low technical load.
• In low volume and without traceability, accountability, or alignment requirements for public policies or regulations.

Then, sticking with ChatGPT + good prompts may be sufficient, as long as the time cost and script-to-script quality variability are accepted.

But when:

• The main input is technical/regulatory documents.
• There is a demand for simple language, transparency, and compliance.
• And content volume grows to the point of requiring processes and indicators.

Alegoria stops being just “another interface for GPT” and becomes a knowledge and narrative production infrastructure that leverages LLMs but goes far beyond what a single chat, no matter how good, can sustainably deliver.