Personas Service - Technical LLM Brief

by Dinis Cruz and ChatGPT Deep Research, 2025/08/22

Overview and Objectives¶

The Personas Service (to be deployed at personas.prod.mgraph.ai) is a stateless microservice that leverages Large Language Models (LLMs) to translate and tailor content for specific target personas. The core idea is to adapt a given message or query so that it is optimally communicated to or from a particular persona, taking into account that persona's language, background knowledge, role, and communication style. This service enables dynamic "persona-aware" translations without training custom models -- it uses existing LLMs via our LLM backend service (e.g. llms.prod.mgraph.ai) for all natural language generation.

Key objectives include:

Effective Communication to Any Persona: Automatically rephrase or summarize information in a way that a target persona (with specific role, expertise, culture, or language) will easily understand and find relevant. For example, explaining a cybersecurity incident in business terms for an executive versus in technical terms for an IT specialist.
Persona-Specific Responses: Allow users to interact with an LLM as a given persona, meaning the LLM will respond in the voice or perspective of that persona. This is useful for simulations (e.g. "What would a CISO say about this risk?") or for generating content with a consistent persona voice.
No Model Training Required: We will not create or fine-tune any new ML models. The service will rely on prompt engineering and the existing LLM infrastructure. This keeps implementation lightweight and focuses on orchestration rather than ML development.
Stateless Microservice Architecture: The service will be stateless -- it will not maintain session data or persona state between requests. Each request contains all necessary inputs (persona definition and content), and the service simply returns the LLM result. Stateless design is aligned with scalability best practices for LLM microservices, as "LLM inference is fundamentally stateless --- each request can be processed independently"[1]. This makes horizontal scaling and AWS deployment (e.g. Lambda or containers) simpler and more robust.
Persona Management: Provide a mechanism to create, edit, and manage persona definitions (likely as JSON objects) that capture how to communicate with or as that persona. Personas can be defined, stored, and retrieved on demand, enabling reuse across requests. We will also explore an LLM-assisted method to generate persona definitions from high-level descriptions to streamline the creation of new personas.

By achieving these objectives, the Personas Service will function as a flexible "persona translator" -- bridging the gap between different knowledge domains, languages, and communication styles, ensuring the right message gets across to the right audience. In essence, it lets developers and systems translate a message for a target persona, or generate a response as if from a target persona, on the fly.

Persona Definition Schema¶

At the heart of this service is the Persona Definition -- a data structure that describes the key attributes of a persona. This definition guides the LLM in adjusting tone, terminology, and detail level for that persona.

A Persona Definition will likely be represented in JSON (or a similar structured format) and could include fields such as:

Name/ID: A unique name or identifier for the persona (e.g. "Cybersecurity Executive - CISO (Portuguese)"). This is mostly for reference.
Role/Job Title: The persona's role or position, which often implies their perspective (e.g. "Chief Information Security Officer", "Finance Board Member", "Software Engineer").
Language and Locale: The language (and possibly locale or culture) the persona prefers. For example, "language": "pt-PT" for a Portuguese speaker, or "language": "en-US" for an American English speaker. The service will output in this language.
Domain Expertise & Knowledge Level: What subject areas the persona is knowledgeable about (and to what depth) vs. areas they are less familiar with. This can guide what terms need explanation. For example:
"expertise": {"cybersecurity": "high", "finance": "low"} for a technical security expert who might not know finance jargon.
Another persona might have "expertise": {"cybersecurity": "low", "finance": "high"} if they are a finance executive who is not well-versed in technical cybersecurity concepts.
Interests/Priorities: What the persona cares about when receiving information. For instance, a CFO persona prioritizes business impact and cost, while a CISO persona prioritizes risk mitigation and technical details. This helps the LLM emphasize certain aspects of the content (financial impact, technical root cause, etc.) when translating or responding.
Communication Style: The tone and style preferred by the persona. This could include formality, brevity, and structure. Examples:
Formal vs. informal tone.
High-level summary vs. in-depth detail.
Empathetic vs. straight-to-business tone.
Whether they prefer visual bullet points, analogies, etc., could also be noted.
Urgency and Frequency Preferences: How the persona handles information urgency. For example, does this persona need critical information upfront ("bottom-line-up-front" style) because they only have time for headlines, or are they comfortable with a detailed narrative? Do they prefer immediate alerts for incidents or periodic summaries?
Additional Context/Traits: Any other relevant attributes, such as cultural context ("values transparency and honesty"), decision-making style, or known biases.

These fields collectively act as a profile that the LLM will use to adjust its output. By feeding such persona attributes into the prompt, the LLM can modulate its response accordingly. As one study on persona-based prompting explains, personas allow LLMs to adjust tone, language complexity, and style for different user types and preferences, leading to more user-centered communication[2]. The persona profile effectively tells the model "here's your audience or role, adapt to them."

Example Persona Definition (JSON):

{
  "id": "persona_ciso_pt",  
  "name": "CISO of ACME Corp (Portuguese)",  
  "role": "Chief Information Security Officer",  
  "language": "pt-PT",  
  "expertise": {
    "cybersecurity": "expert",
    "finance": "basic"
  },  
  "priorities": ["cyber risk reduction", "incident response", "technical accuracy"],  
  "communication_style": {
    "tone": "professional",
    "detail_level": "high",
    "style": "direct and technical"
  },  
  "urgency_preference": "immediate updates on major incidents"
}

Another example might be a Board Executive persona in English:

{
  "id": "persona_board_exec_en",  
  "name": "Board Member (Finance)",  
  "role": "CFO, Board of Directors",  
  "language": "en-UK",  
  "expertise": {
    "cybersecurity": "low",
    "finance": "expert"
  },
  "priorities": ["business impact", "regulatory compliance", "reputation risk"],  
  "communication_style": {
    "tone": "formal",
    "detail_level": "summary-focused",
    "style": "business-centric and concise"
  },
  "urgency_preference": "focus on key points first, follow-up details if needed"
}

These are illustrative; the schema can be refined. The key is that the persona profile provides enough information for the LLM to infer how to present or interpret information for that persona. The service will likely maintain a schema definition to validate persona objects. It may also include a version or timestamp if we plan to update personas over time.

Persona Storage and Retrieval: Since the Personas service itself is stateless, persona definitions are not permanently stored in the service instance. Instead, persona data can be:

Provided directly in the request (as a JSON payload or reference). A client can include the full persona object with each API call that needs it.
Referenced by an ID/URL: The service could accept a reference (like an ID or a URL) to fetch the persona definition from an external source (e.g., a database, an object storage link, or a separate "persona registry" service). For example, persona_id: "persona_ciso_pt" could be resolved by the service via a known config store or HTTP GET from a given URL that returns the JSON. We will leverage existing OSBot/TypeSafe configuration mechanisms if available, so that the service can fetch and safely use persona data without maintaining state internally.
The use of type-safe primitives and careful input handling will ensure that persona definitions (which might contain natural language descriptions) do not inadvertently introduce prompt-injection or malformed prompts. Essentially, the persona fields will be encoded or inserted into the LLM prompt in a controlled manner (e.g., as a structured system prompt or via a template) so that the model interprets them as context, not as user instructions.

Service Modes of Operation¶

The Personas Service offers two primary modes of operation, corresponding to two ways of using a persona profile with the LLM: (1) Translate to Persona, and (2) Respond as Persona. These fulfill different needs in the communication workflow.

1. Translate to Persona (Persona as Audience)¶

In this mode, the service takes an input message and rewrites or translates it for the target persona. The assumption is that the input content is written from a general or original perspective, and we want to tailor it so that the target persona will understand it fully and be addressed appropriately. Essentially, this treats the persona as the audience/recipient of the message.

Input: The client provides a piece of content (text) that needs to be translated for the persona, along with the persona definition (or an ID for it). For example, an input might be: "There's been a ransomware incident in Division X that is impacting the P&L statement."
Operation: The service will construct a prompt to the LLM that says, in effect, "Rephrase or explain this message in a way that [Persona X] would understand and find relevant." It will embed the persona's attributes (language, expertise, etc.) into the prompt as context. The underlying LLM (via the llms.mgraph.ai backend) will generate a rewritten version of the content tailored to the persona.
Output: A transformed version of the message, addressed to the persona. The output will be in the persona's language and pitched at the right level of detail and tone.

Example: Consider the message about a ransomware incident affecting profit & loss.\ - For the CISO persona (Portuguese, cybersecurity expert), the original terms like "ransomware" are well-understood (no need to explain what ransomware means), but business terms like "P&L" might be unfamiliar. The service might output (in Portuguese) a message that still calls it "ransomware" but perhaps clarifies "impacting the financial results (lucros e perdas)" since P&L might need expansion. It would also likely include technical details (since a CISO would want to know specifics of the incident) and be fairly direct about the cybersecurity aspects.\ - For the Board Executive persona (English, finance expert), the service would do the opposite: it might not bother explaining "P&L" (because a CFO knows it means Profit and Loss), but it would explain "ransomware" in more accessible terms (e.g. "a type of cyber attack that encrypts data and demands a ransom"). The tone would be high-level, focusing on business impact ("it has caused downtime in Division X and could affect Q4 financials") rather than technical minutiae. It might be more formal and include a suggestion of actions or reassurances, fitting how one would brief a board member.

These tailored outputs ensure each persona gets the information in the way that makes sense to them. If the same original message is run through Translate to Persona for multiple personas, we get multiple versions, each appropriate for its audience. This is extremely useful in scenarios like incident communication, where different stakeholders (IT teams, executives, customers) must all be informed with the same base facts but with different emphasis.

From an implementation standpoint, translate mode will typically use a prompt pattern like:

System prompt: "You are an assistant that helps translate messages for specific audiences."\ Instruction: "Please translate/reframe the following message for [PersonaName], who is a [persona role and traits]. The output should be in [PersonaLanguage] and communicated in a style appropriate for this persona."\ Original message: "...(the content)...".

We will fine-tune the exact prompt for optimal results. The LLM will then produce the rewritten message.

2. Respond as Persona (Persona as Speaker)¶

In this mode, the service enables clients to ask a question or present a scenario, and get an answer as if the persona itself is responding. Here, the persona serves as the speaker or responder. This effectively lets the LLM role-play as the persona, using the persona's knowledge and communication style to generate an answer or commentary.

Input: A user query or prompt that the persona should respond to, plus the persona definition (or reference). For example, a query might be: "What are the biggest concerns from this ransomware incident?" and the persona could be the Board Executive.
Operation: The service will prompt the LLM with the persona's profile and instruct it to answer the query in the first person (or appropriate voice) of that persona. Essentially: "Imagine you are [Persona X]. Given the following question/situation, provide the response as if you are Persona X, using their knowledge, tone, and perspective."
Output: A response or answer text generated by the LLM, written in the persona's voice or viewpoint. It should reflect the persona's priorities and expertise.

Example: Using the same ransomware scenario:\ - If we ask "How should we handle this incident?" and choose the CISO persona, the Respond as Persona mode would yield an answer that a typical CISO might give. In Portuguese (since that persona's language is pt-PT), it might say: "Como CISO, minha recomendação é primeiro isolarmos os sistemas afetados pelo ransomware, em seguida ..." and go on to detail technical incident response steps, perhaps referencing security teams, forensics, etc. The answer would be detailed and action-oriented from a security standpoint.\ - If we instead ask the Board Executive persona the same question, the response might come as a formal statement in English, like: "From a board perspective, I would want to ensure that our response team is containing the incident and that we have engaged cybersecurity experts. Our priority is to minimize financial and reputational impact...". This answer would be less technical, more strategic, and may include a request for frequent updates or assurance that stakeholders (investors, regulators) are kept informed -- reflecting a board-level concern.

This Respond as Persona capability can be used to simulate conversations with different roles. It's akin to having an expert or stakeholder "speak" via the LLM. For instance, a development team could ask "What would the CFO think about investing in this new security software?" and get a plausible answer crafted in a CFO-like tone. It's a powerful way to anticipate questions or viewpoints from various personas. It also overlaps with the concept of persona-based chatbots (where the chatbot has a fixed persona).

The prompt structure for this mode will be something like:

System prompt: "You are impersonating [PersonaName] -- [PersonaDescription]. Answer user queries as this persona."\ User prompt: "\<the user's question or statement>" (which might include scenario details).

The LLM then produces the persona's answer. We will ensure the prompt includes all relevant persona info so that the model stays in character (e.g. using first person if appropriate: "I, as [role], think that..." or simply a knowledgeable tone).

Chaining Both Modes (Double Translation Workflow)¶

It's worth noting that these two modes can be combined in sequence for optimal communication loops. For example, one could first use Translate-to-Persona to tailor a message for a persona, then use Respond-as-Persona by feeding that translated message to get the persona's hypothetical reply. This two-step exchange ensures that the persona fully "understands" the initial message as it was intended.

In fact, a suggested best practice is:\ - Take an important piece of information, translate it to the target persona's framing, then - Immediately have the persona respond (perhaps asking clarifying questions or acknowledging).

If the persona's response seems off or indicates misunderstanding, it's a signal that the translation might need adjustment. Ideally, though, by using the persona's own terminology and context in the first step, the persona (in the second step) will easily comprehend and respond appropriately. This loop can improve clarity of communication across knowledge domains.

Architecture and Integration¶

The Personas Service will be implemented as a lightweight web service (e.g., a Python FastAPI or Node.js Express service, given our stack preferences) that exposes RESTful endpoints or an API for the above functionalities. Key architectural considerations:

Stateless Design: As mentioned, each API call is independent. Any needed context (like persona details or conversation history) must be supplied in the request. The service itself will not store session data. This statelessness allows easy scaling (we can run multiple instances behind a load balancer) and aligns with cloud deployment on AWS Lambda or containers[1]. If longer conversations are needed, the client would have to manage the conversation context (possibly by including past Q&A as part of the prompt or using the underlying LLM service's features).
Endpoints: We anticipate endpoints such as:
POST /translate -- for Translate-to-Persona mode. The payload would include either a persona ID or full persona data, and the message to translate. It returns the translated message.
POST /respond -- for Respond-as-Persona mode. Payload includes persona info and the user's query or prompt. Returns the persona's answer.
POST /persona (or /personas) -- for creating a new persona profile. This could accept a JSON definition to store (if we maintain a store or forward it to a config service). Given statelessness, this might actually call an underlying config storage (like an AWS DynamoDB or our OSBot config) to save the persona. Alternatively, the client might just manage persona JSON externally and not store on the service at all, using this endpoint only to validate schema or to run LLM-assisted generation (see next point).
PUT /persona/{id} -- update an existing persona definition (if storing).
GET /persona/{id} -- retrieve a persona definition (if we implement a store).
These are optional if we decide the service itself won't permanently hold personas. They could be omitted in favor of managing personas elsewhere, with the service just consuming them.
Persona Creation via LLM (Optional Enhancement): A special endpoint like POST /persona/generate could take a description or requirements in natural language and utilize the LLM to produce a draft persona JSON. For example, one could POST: {"description": "A persona of a CTO who is very technical, prefers brief summaries, and speaks French."} and the service would call the LLM to fill out a persona template (role: CTO, language: French, likely high tech expertise, etc.). This is a convenience feature to help define personas without manually writing JSON. The returned JSON can then be reviewed and refined by the user. This feature again uses the existing LLM backend and prompt engineering: e.g., "Given the following description, output a JSON persona profile matching our schema."
Integration with LLM Backend: The service will act as a client to the llms.prod.mgraph.ai service. That backend presumably provides a unified API to various LLMs (OpenAI GPT-4, etc.). Our service will format requests to it, likely including: a system prompt (with persona context), the user prompt or content, and any parameters (like temperature, max tokens). It will then receive the LLM's generated text and forward that back to the caller. We must handle errors from the LLM service (network issues, model errors, token limits, etc.) and return appropriate error responses or fallbacks. Since our service logic is relatively simple (prompt construction and forwarding), the main errors will be from upstream or input validation.
Prompt Engineering: Crafting the right prompts is crucial for quality output. We will maintain prompt templates for each mode. For example, for translate mode: "Act as a communications expert translating for [persona description]...", and for respond mode: "Act as [persona name] and respond to this query...". We will likely include the persona's data in the system prompt so that the model always has that context. The user's actual message or question will be the user prompt part. We must ensure the model's output is in the correct language (we can explicitly instruct the language).
Type Safety and Security: If using the OSBot Type-Safe primitives, we will ensure that when inserting user-provided strings (like the content to translate), we quote or otherwise neutralize any special tokens that could be misinterpreted as prompt instructions. The persona service should also sanitize or limit any persona definitions if they come from untrusted sources, to avoid malicious personas that could hijack the prompt (this is a form of prompt-injection prevention by not blindly concatenating strings). Using a structured approach (like inserting persona fields in a JSON-like format or clearly labeling them in the system prompt) can help the model distinguish persona metadata from conversation content.
Performance: The service itself will have minimal overhead (just JSON parsing, a possible fetch of persona data, and the LLM API call). The heavy lifting is the LLM inference which happens in the backend service. We should design with concurrency in mind (allow multiple requests to be processed in parallel if the backend can handle it). If needed, we might implement a simple cache for recent translations or responses to avoid duplicate LLM calls, but given that persona outputs are highly context-specific, caching may have limited use except in repeated identical queries. Logging and monitoring should be in place to track usage and latency.
AWS Deployment: We expect to deploy this as a container or serverless function on AWS. The lack of state means we don't need a database for core functionality (unless we add an internal store for personas). We will use environment configuration for things like the URL/credentials of the LLM backend, and possibly for specifying where to retrieve persona definitions if by reference (e.g., an S3 bucket or a config API). The service will be accessible at personas.prod.mgraph.ai (behind authentication if required, or within a VPC if it's internal).

Usage Example Walk-through¶

To illustrate how everything comes together, let's walk through a realistic usage scenario step by step:

Scenario: There has been a security incident (ransomware attack) at a company. The technical incident report is written in a very IT-focused way. We need to communicate this to two stakeholders: Alice (the CISO of a subsidiary, who speaks Portuguese and is very technical) and Bob (a board member/CFO, who is English-speaking and non-technical). We'll use the Personas Service to generate the communications.

Defining Personas: Suppose we have pre-defined persona profiles for Alice and Bob. Alice's persona (ID persona_ciso_pt) and Bob's persona (ID persona_board_exec_en) are stored in our system or available as JSON. If not already defined, we could create them via the service:
We call POST /persona with Alice's details (as per earlier example JSON). Similarly for Bob. The service either stores them or returns an ID for immediate use. (If the service is truly stateless and not storing, we skip this step and just prepare the JSON in our client.)
Translating the Incident Report: The original incident report text might be: "Division X experienced a ransomware attack compromising several servers. The attack has impacted the P&L --- financial reporting for Q4 might be delayed. Technical teams are working to contain the malware and restore backups."
To generate Alice's version, we call POST /translate with persona_id = persona_ciso_pt (or the full persona JSON for Alice) and the above text.
The Personas Service fetches Alice's persona definition (if given by ID), then creates a prompt for the LLM: "Translate the following for [Alice's persona details]" + original text.
The LLM returns a Portuguese response, perhaps: "A Divisão X sofreu um ataque de ransomware que comprometeu vários servidores. Este ataque afetou os resultados (lucros e perdas) --- pode haver um atraso nos relatórios financeiros do Q4. As equipas técnicas estão a trabalhar para conter o malware e restaurar os backups."
- Notice it kept the word ransomware (no need to explain to Alice), but it translated P&L to a phrase "resultados (lucros e perdas)" to clarify for a Portuguese reader. It's detailed yet in a professional tone.
For Bob's version, we call POST /translate with persona_id = persona_board_exec_en and the same original text.
The LLM might return an English output aimed at an executive: "Division X has suffered a ransomware cyber-attack affecting several servers. This has impacted our operations and potentially the Q4 profit-and-loss statements. Our IT team is actively containing the attack and working to restore data from backups. We will likely experience some downtime, but mitigation efforts are underway to protect financial reporting."
- In Bob's version, jargon like "ransomware" is briefly framed as "cyber-attack," and the significance (impact on operations and reporting) is highlighted. It's written in a slightly more explanatory and reassured tone for an executive audience.
Review and Send: The translated messages for Alice and Bob can now be sent to them. Each will receive a communication that is immediately understandable and relevant to their perspective. Alice gets the info in her language with technical details; Bob gets a high-level English briefing with business context.
Optional -- Persona Response: Suppose we want to know how Alice (the CISO) might respond or what questions she might have. We can take the message we sent her and ask the service to respond as Alice.
Call POST /respond with persona_id = persona_ciso_pt and input: "\<The translated message we gave Alice> \n\n What is your response or what actions will you take?" (or simply assume she read it and ask the persona for comments).
The service will have the LLM role-play Alice. The output might be a response in Portuguese like: "Obrigado pelo aviso. Vou assegurar-me de que a nossa equipa de segurança em Portugal está pronta para ajudar e que revisamos imediatamente os nossos sistemas em busca de sinais semelhantes de ataque. Por favor, mantenha-me atualizado sobre o progresso e qualquer impacto financeiro confirmado."
This shows the persona (Alice) acknowledging the info and outlining steps (ensuring her team is on alert, requesting updates on impact). This kind of simulation can be useful to anticipate needs or follow-ups from that persona.
Lifecycle: After usage, since the service is stateless, it doesn't retain anything about this interaction. If another incident occurs, the same steps can be repeated. New personas can be added at any time by defining new JSON profiles and using them in requests. The existing LLM backend can be scaled as usage grows, and because each request is independent, we can easily distribute load.

Considerations and Future Enhancements¶

Accuracy and Validation: The quality of outputs depends on the LLM's understanding from the prompts. We should test with various persona profiles and content to ensure the instructions are followed (e.g., the model indeed uses the correct language and simplifies/explains as expected). Some iteration on prompt wording is expected. Additionally, it might be wise to include a post-processing step to verify language (for example, ensure no English leaks into a Portuguese answer for Alice's case). The service could detect language mismatches or ask the LLM explicitly to only output in the target language.
Security: As this service deals with potentially sensitive internal messages (like incident reports), it should enforce authentication/authorization such that only authorized systems or users can call it (especially if it's exposed as an API). Also, careful logging (without storing raw sensitive content) should be practiced. LLM prompts and responses might be logged for debugging but possibly need to be sanitized or kept secure.
Persona Library and UI: In the future, we might build a small UI or repository for managing persona profiles (so non-developers can create/edit personas easily). For now, JSON definitions suffice for a backend service.
No Custom Model but Possible Fine-Tuning: While initially we commit to no custom models, if the persona translations become a heavily used feature, one could consider fine-tuning a model or using retrieval-augmented generation to improve consistency (for example, ensuring certain terms are always translated a specific way for a persona). However, this is outside the current scope and likely unnecessary given powerful general models available.
Multi-Persona or Group Communication: This service currently focuses on one persona at a time. An interesting extension later could be handling multiple personas in one go (for instance, produce outputs for all relevant personas for an incident). The client can of course loop through personas, but a batch call might be convenient. Additionally, we could simulate a round-table discussion by cycling through respond as persona with different personas to emulate a conversation (though managing that is more complex).
Citations and Fact-Checking: If the content being translated contains factual data or specific terms, the LLM should ideally not hallucinate or alter facts during translation. Prompting should emphasize "do not change the factual content, only rephrase". In testing, we must verify that numerical data or specific names remain correct in the output.

Conclusion¶

The Personas Service will fill a valuable role in making communication across diverse stakeholders more efficient and tailored. By harnessing LLM capabilities in a stateless microservice, we enable on-demand personalization of messages -- whether it's simplifying technical jargon for executives or adding technical depth for experts, and switching languages or tones as needed. Persona-driven communication has been shown to increase engagement and clarity, as the messaging is aligned with the audience's expectations[2]. Our service will provide a programmatic way to achieve this alignment.

In summary, the Personas Service allows any application or workflow to "speak the language" of its target personas, without manual re-writing for each audience. It uses the power of large language models to do the heavy lifting of translation and rephrasing, following persona profiles that capture the essence of who the audience or speaker is. This not only saves time but ensures consistency and appropriateness of communications across an organization. By keeping the service stateless and leveraging existing LLM infrastructure, we also ensure that it is scalable, maintainable, and easy to integrate into our ecosystem (e.g., hooking into incident response tools, report generators, chat interfaces, etc.).

Going forward, developers and LLM engineers working on this service should focus on robust prompt engineering, thorough testing with different personas, and a simple API design for ease of use. With those in place, personas.prod.mgraph.ai will become a key component for persona-aware AI interactions within our platform, enabling more effective and nuanced communication powered by AI.

[1] Building LLM-Powered Microservices With Kubernetes on AWS

https://dzone.com/articles/reliable-llm-microservices-kubernetes-aws

[2] LLM Personas Prompting Makes Personalized AI Simple

https://vidpros.com/llm-personas-prompting/