This is a specific vision—a cloud-based, opinionated creative writing mentor (“Miss Sherbourne”) that combines structured prompt engineering, user-guided RAG, and interactive project scaffolding. Here’s how to architect it:

System Architecture Overview

#

flowchart TB
    A[User Onboarding] --> B[Uber-Prompt Builder]
    B --> C[Content Upload: User + Public Domain Works]
    C --> D[Cloud RAG Database]
    D --> E[Miss Sherbourne: Interactive Session]
    E --> F[Project Artifacts: Chapters, Notes, Critiques]
    F --> G[Export: Novel, Script, etc.]

Key Components & Tech Stack

#

1. Web App (Cross-Platform)

#

• Frontend: Next.js (React) + TailwindCSS
• State Management: Zustand (for session tracking)
• Voice/Text: Web Speech API (transcription) + Whisper (backup)

2. Uber-Prompt Engineering Workflow

#

• Step-by-Step Q&A to define:
  • Genre
  • Tone (e.g., “Gritty Victorian” vs. “Whimsical SF”)
  • Narrative style (1st/3rd person, tense)
  • Miss Sherbourne’s persona (strictness, focus areas)
• Output: A structured prompt like:

  "You are Miss Sherbourne, a strict creative writing instructor specializing in [genre]. 
  Your feedback is blunt but constructive. Prioritize [aspects]. Never allow [taboos]. 
  Current project: [summary]. Next, ask the user 3 questions about their protagonist’s flaws."
  

3. Cloud RAG System

#

• Database: PostgreSQL + pgvector (for embeddings)
• Embedding Model: text-embedding-3-small (OpenAI) or bge-small (open-source)
• Retrieval: Hybrid search (keyword + vector) for past interactions/uploaded docs

4. Training Pipeline (User Data)

#

• Fine-Tuning: LoRA adapters per-user (via Modal or RunPod)
  • Base model: Mistral 7B or Llama 3
  • Datasets: User’s past works + public domain (Project Gutenberg)
• Isolation: Dedicated storage per user (e.g., AWS S3 buckets)

5. Miss Sherbourne’s Personality Engine

#

• Critique Templates:

  def generate_critique(text):
      if "plot hole" in analyze_text(text):
          return "This section lacks coherence. [EXAMPLE FROM RAG]. Try: [SUGGESTION]"
      elif "passive voice" in analyze_text(text):
          return "Too limp! Strike with active verbs like Hemingway: [RAG EXAMPLE]"
  

• Opinionated Defaults:
  • “You haven’t described the setting. Start with 3 sensory details.”
  • “Your antagonist feels shallow. Here’s a character questionnaire.”

User Flow Example

#

1. Onboarding:
   • User selects “Start New Novel” → Uber-Prompt builder asks 10 questions.
2. Upload:
   • Drag/drop past works + public domain inspirations.
3. Session:

   Miss Sherbourne: "Your protagonist lacks a visible wound. How did their childhood shape them?"
   User: [Types/Voices response]
   Miss S: "Too vague! Here’s how Dickens would do it: [RAG-retrieved excerpt]."
   

4. Output:
   • Auto-organized project with chapters, critiques, and references.

Tech Stack Details

#

Critical Features

#

1. Strict Persona Guardrails:
   • Prevent Miss Sherbourne from becoming too nice:

     prompt += "Never use platitudes. Demand revision if prose is clichéd."
     

2. Project Memory:
   • Every interaction is embedded and retrievable.
   • Example: “Last week you said the theme is ‘redemption’—why does this scene ignore that?”
3. Export Formats:
   • Manuscript (.docx), Lore Bible (.md), Audio Recap (ElevenLabs).

Copyright & Privacy

#

• User Data: Never used for training beyond their instance.
• Public Domain: Pre-loaded CC0/Project Gutenberg texts (filtered for relevance).
• ToS Clause:

  "You retain all rights to uploaded content. Miss Sherbourne’s feedback is AI-generated suggestion."
  

Why This Works

#

• Structured Creativity: Writers get both freedom and discipline.
• Voice-Driven: Feels like collaborating with a real editor.
• Scalable: LoRA adapters are tiny (~10MB/user).

Challenges & Mitigations

#

Next Steps

#

1. Prototype the Uber-Prompt Builder:
   • Use a form lib (React Hook Form) to guide users.
2. Mock Miss Sherbourne:
   • Hardcode critiques for a test novel chapter.
3. Build a Minimal RAG:
   • Supabase + OpenAI embeddings (free tier).

Want to dive into any of these? For example:

• A sample Uber-Prompt for “Gothic Horror”.
• How to structure the RAG database schema.
• Voice interaction design patterns.

=========================

Project Sherbourne: The Opinionated AI Writing Mentor

#

An AI-powered creative writing platform that combines structured guidance, personalized feedback, and uncompromising editorial standards

graph TD
    A[Writer] --> B[Uber-Prompt Builder]
    B --> C[Miss Sherbourne Persona]
    C --> D[Interactive Writing Session]
    D --> E[Cloud RAG System]
    E --> F[Project Artifacts]
    F --> G[Export Manuscript]
    A --> H[Content Upload]
    H --> E

Product Vision

#

Sherbourne transforms creative writing by pairing AI capabilities with a structured pedagogical approach. Unlike generic writing assistants, Sherbourne acts as a digital writing mentor with strong opinions and high standards - “Miss Sherbourne” - who guides writers through the creative process while maintaining their unique voice.

Core Features

#

• Persona-Driven Interaction: Miss Sherbourne’s strict British mentor persona
• Uber-Prompt Engineering: Guided Q&A builds custom project blueprints
• Multi-Source RAG: Unified knowledge from user content + public domain works
• Project Memory System: Captures all interactions as writing assets
• Voice-to-Process: Dictate conversations with your AI mentor
• Style Preservation: Fine-tuned adaptation to writer’s unique voice

Target Users:

• Novelists seeking developmental editing
• Screenwriters needing structural guidance
• Content creators combating writer’s block
• Writing students desiring professional critique

Technical Specification

#

System Architecture

#

graph LR
    A[Web Client] --> B[API Gateway]
    B --> C[Prompt Engineering Service]
    B --> D[RAG Orchestrator]
    B --> E[Voice Processing]
    D --> F[Vector DB]
    D --> G[LLM Gateway]
    G --> H[Fine-Tuning Cluster]
    F --> I[Cloud Storage]

1. Core Components

#

Uber-Prompt Builder

#

• Function: Creates project-specific instruction sets through guided dialog
• Process:
  1. Genre/Tone Assessment (20+ question profile)
  2. Narrative Voice Calibration (writing sample analysis)
  3. Persona Configuration (strictness, focus areas)
  4. Project Scaffolding (chapter-by-chapter or discovery mode)
• Output: Structured JSON prompt with:

  {
    "persona": "strict_british_mentor",
    "project_rules": ["no_adverbs", "show_dont_tell"],
    "current_focus": "character_development",
    "next_steps": ["setting_description", "conflict_escalation"]
  }
  

Miss Sherbourne Persona Engine

#

• Core Principles:
  • Blunt but constructive criticism
  • Proactive questioning to uncover plot holes
  • Reference-driven suggestions (RAG-powered examples)
• Implementation:
  • Custom system prompt with 3 personality layers:

    base_persona = "Strict Cambridge writing tutor"
    project_rules = uber_prompt['constraints']
    session_context = last_3_interactions
    

  • Dynamic tone adjustment based on writer resistance

Project Memory RAG System

#

• Data Layers:

  Layer	Content	Storage
  L1	User conversations	PostgreSQL
  L2	Uploaded manuscripts	S3 + pgvector
  L3	Public domain references	FAISS index

• Retrieval Features:
  • Cross-references current writing with similar passages
  • Flags continuity errors against earlier chapters
  • Surfaces relevant critique templates

2. AI Infrastructure

#

• Base Models:
  • Mistral 7B (fine-tuning)
  • GPT-4-Turbo (complex analysis)
  • Claude 3 (long-context processing)
• Fine-Tuning Approach:
  • Per-user LoRA adapters (avg. 12MB)
  • Progressive training during uploads
  • Style transfer validation via BERTScore
• RAG Pipeline:

  sequenceDiagram
      Writer->>+RAG Engine: Submit draft paragraph
      RAG Engine->>+Vector DB: Semantic search
      Vector DB-->>-RAG Engine: Top 5 matches
      RAG Engine->>+Critique Model: Generate feedback
      Critique Model-->>-RAG Engine: Annotated response
      RAG Engine->>+Writer: Miss Sherbourne's critique
  

3. Data Architecture

#

Storage Schema:

erDiagram
    USERS ||--o{ PROJECTS : has
    PROJECTS ||--o{ UBER_PROMPT : uses
    PROJECTS ||--o{ VERSIONS : contains
    VERSIONS ||--o{ INTERACTIONS : contains
    INTERACTIONS ||--o{ RAG_REFERENCES : uses
    PROJECTS ||--o{ UPLOADS : includes

• Security Measures:
  • End-to-end encryption for user content
  • VPC-isolated training environments
  • GDPR-compliant data handling
• Copyright Safeguards:
  • Upload content validation (copyright detection API)
  • Public domain verification layer
  • Watermarked AI outputs

4. UX Workflow

#

1. Onboarding:

   • Create writer profile (voice/style preferences)
   • Select project type (novel, screenplay, memoir)
   • Build Uber-Prompt through 10-min Q&A

2. Content Ingestion:

   • Upload past works (PDF, DOCX, EPUB)
   • Select public domain supplements
   • Voice-record writing concepts

3. Writing Session:

   Miss S: "Your protagonist's motivation remains unclear.  
            Compare with Austen's Elizabeth Bennet: [RAG excerpt].  
            How does your character react to betrayal?"  
   [User dictates response]  
   Miss S: "Too emotional! Refine using objective correlatives."  
   

4. Project Export:

   • Manuscript in standard formats
   • “Writer’s Bible” with all interactions
   • Audio summary of developmental progress

DeepThink Insights

#

Psychological Foundations

#

Structured Creativity Paradox:
Research shows constraints boost creativity (Stokes, 2006). Miss Sherbourne’s strictness leverages:

• Cognitive Scaffolding: Breaking complex tasks into manageable critiques
• Accountability Effect: Anthropomorphized mentor increases commitment
• Productive Discomfort: Optimal frustration level stimulates growth

Technical Innovations

#

1. Persona Continuity System:

   • Tracks personality consistency through:

     persona_fidelity = cosine_similarity(
         current_response_embedding, 
         persona_base_embedding
     )
     

   • Triggers recalibration when fidelity < 0.82

2. Anticipatory RAG:

   • Predicts needed references based on:
     • Current writing phase (exposition/conflict/resolution)
     • Writer’s recurring weaknesses
     • Narrative arc position

3. Voice-Print Adaptation:

   • Converts speech patterns to stylistic preferences:

     Speech Trait	Writing Adaptation
     Fast tempo	Short sentences
     Pauses	Em-dash usage
     Pitch variance	Emotional tone

Ethical Considerations

#

• Agency Preservation: Always clear when writer overrides suggestions
• Bias Mitigation:
  • Public domain corpus balanced across eras/genders
  • Cultural sensitivity filters for critique templates
• Burnout Prevention: Session pacing algorithms detect frustration

Roadmap

#

“Sherbourne doesn’t just assist writing - it cultivates writers. By combining AI capabilities with structured pedagogy, we create the digital equivalent of a master-apprentice relationship scaled for the modern creator.” - Project Manifesto

Technical Appendix Available Upon Request

Technical Appendix: Project Sherbourne

#

Detailed specifications for implementation

graph TD
    A[Web Client] --> B[Edge Services]
    B --> C[Core AI Services]
    C --> D[Data Layer]
    D --> E[Cloud Infrastructure]

1. System Architecture

#

Component Diagram

#

graph LR
    WC[Web Client] --> AG[API Gateway]
    AG --> ID[Interaction Dispatcher]
    ID --> UP[Uber-Prompt Service]
    ID --> RAG[RAG Orchestrator]
    ID --> VP[Voice Processor]
    UP --> DB1[(Prompt Registry)]
    RAG --> VDB[(Vector DB)]
    RAG --> LM[LLM Gateway]
    VP --> STT[Speech-to-Text]
    LM --> FT[Fine-Tuning Cluster]
    FT --> OBJ[Object Storage]

Technology Stack

#

2. API Specifications

#

Core Endpoints

#

Uber-Prompt Builder Service
POST /v1/prompt-engineer

{
  "project_type": "novel",
  "genre": "gothic_horror",
  "writing_sample": "The castle loomed...",
  "user_preferences": {
    "strictness_level": 9,
    "focus_areas": ["character_development", "atmosphere"]
  }
}

Response:

{
  "uber_prompt_id": "UP_abcd1234",
  "structured_prompt": {...},
  "first_instructions": "Begin by describing the protagonist's deepest fear..."
}

RAG Interaction Service
POST /v1/rag-interaction

{
  "project_id": "proj_xyz987",
  "user_input": "Here's my opening paragraph...",
  "conversation_history": [...],
  "mode": "critique"
}

Response:

{
  "response": "Your description lacks sensory details. Consider how Poe would...",
  "rag_references": [
    {"source": "user_upload_123", "excerpt": "The floorboards groaned..."},
    {"source": "gutenberg_789", "excerpt": "A faint chill crept up..."}
  ],
  "next_step": "character_interview"
}

WebSocket Services

#

Real-time Writing Session
wss://api.sherbourne.app/v1/session/{session_id}

• Bi-directional streaming of:
  • Voice transcription chunks
  • Real-time critique suggestions
  • RAG context updates
• Heartbeat interval: 15s
• Payload structure:

  message SessionUpdate {
    string session_id = 1;
    oneof content {
      AudioChunk audio = 2;
      TextFragment text = 3;
      RagContextUpdate rag = 4;
      LlmResponse response = 5;
    }
  }
  

3. Data Schemas

#

Core Database Schema

#

erDiagram
    users ||--o{ projects : "1:N"
    projects ||--o{ uber_prompts : "1:1"
    projects ||--o{ interactions : "1:N"
    projects ||--o{ uploads : "1:N"
    interactions ||--o{ rag_references : "1:N"
    
    users {
        string user_id PK
        string email
        datetime created_at
    }
    
    projects {
        string project_id PK
        string user_id FK
        string project_type
        jsonb metadata
    }
    
    uber_prompts {
        string prompt_id PK
        string project_id FK
        jsonb structured_prompt
    }
    
    interactions {
        string interaction_id PK
        string project_id FK
        text user_input
        text ai_response
        jsonb rag_context
        datetime timestamp
    }
    
    uploads {
        string upload_id PK
        string project_id FK
        string file_path
        string source_type "user|public_domain"
        vector embedding
    }

Vector DB Indexing

#

pgvector Configuration

CREATE TABLE document_embeddings (
    id SERIAL PRIMARY KEY,
    project_id VARCHAR(36) NOT NULL,
    content TEXT NOT NULL,
    embedding VECTOR(1536),  -- OpenAI dim size
    metadata JSONB
);

CREATE INDEX ON document_embeddings 
USING ivfflat (embedding vector_cosine_ops) 
WITH (lists = 100);

Embedding Models

• Primary: text-embedding-3-small (1536-dim)
• Fallback: BAAI/bge-small-en-v1.5 (384-dim)

4. AI Infrastructure

#

Fine-Tuning Pipeline

#

sequenceDiagram
    participant U as User
    participant S as Storage
    participant FT as Finetune Service
    participant M as Model Registry
    
    U->>S: Upload writing samples
    S->>FT: Trigger preprocessing
    FT->>FT: Chunk & clean text
    FT->>FT: Generate QLoRA adapter
    FT->>M: Store adapter (user-specific)
    M->>S: Link to project metadata

Training Parameters

base_model: "mistralai/Mistral-7B-v0.1"
quantization: "4bit"
lora_config:
  r: 16
  lora_alpha: 32
  target_modules: ["q_proj", "v_proj"]
training_args:
  per_device_train_batch_size: 4
  gradient_accumulation_steps: 8
  learning_rate: 2e-5
  max_steps: 500
  logging_steps: 25

RAG Retrieval Algorithm

#

def hybrid_retrieval(query: str, project_id: str, k: int = 5):
    # Text embedding
    vector_results = vector_db.similarity_search(
        query, 
        k=k*2, 
        filter={"project_id": project_id}
    )
    
    # Keyword boost
    keyword_results = bm25_retriever.retrieve(
        query, 
        top_n=k*3,
        project_filter=project_id
    )
    
    # Fusion scoring
    combined = reciprocal_rank_fusion(
        vector_results, 
        keyword_results,
        k=k
    )
    
    # Freshness boost for recent uploads
    return apply_recency_bias(combined, weight=0.3)

5. Security Architecture

#

Data Protection Measures

#

Encryption Scheme

graph LR
    C[Client] -- TLS 1.3 --> G[API Gateway]
    G -- AES-256-GCM --> S[Services]
    S -- Field-level Encryption --> DB[(Database)]
    DB -- Key Vault --> KMS[HSM-backed KMS]

Access Control Model

• RBAC with project-based isolation
• JWT claims structure:

  {
    "sub": "user_123",
    "proj": "proj_abc",
    "permissions": {
      "rag": "read_write",
      "training": "trigger",
      "exports": "read"
    }
  }
  

Compliance Features

#

• Data Residency: Per-region storage buckets
• Right to Forget: Cascading deletion triggers
• Audit Trail: Immutable interaction logs
• Copyright Scanning:

  def validate_upload(content: str) -> bool:
      if copyright_scanner.match(content, threshold=0.85):
          return False
      return public_domain_verifier.verify(content)
  

6. Deployment Topology

#

Cloud Architecture

#

graph TD
    CDN[Cloudflare CDN] --> GW[API Gateway]
    GW -->|Request Routing| K8s[Kubernetes Cluster]
    
    subgraph K8s Cluster
        direction LR
        subgraph Namespace: AI
            FT[Finetune Job]
            RAG[RAG Service]
            LLM[LLM Gateway]
        end
        
        subgraph Namespace: Data
            PG[PostgreSQL HA]
            VC[Vector DB Pool]
            MC[Memcached]
        end
    end
    
    K8s --> ST[Object Storage]
    ST --> B1[User Data Buckets]
    ST --> B2[Public Domain Corpus]

Scaling Configuration

Estimated Resource Costs

7. Quality Assurance

#

Testing Framework

#

AI-Specific Validation

def test_persona_consistency():
    responses = generate_test_responses(scenarios=100)
    fidelity_scores = []
    
    for resp in responses:
        score = cosine_similarity(
            embed(resp), 
            embed(BASE_PERSONA_PROMPT)
        )
        fidelity_scores.append(score)
    
    assert np.mean(fidelity_scores) > 0.85, "Persona drift detected"


def test_rag_relevance():
    test_queries = load_test_dataset("writing_scenarios")
    precision_scores = []
    
    for query in test_queries:
        results = hybrid_retrieval(query)
        precision = calculate_ndcg(results, ideal_references)
        precision_scores.append(precision)
    
    assert np.mean(precision_scores) > 0.92, "RAG quality below threshold"

Performance Benchmarks

#

Appendix Revision: 1.2
Last Updated: 2025-06-25
Confidentiality: Internal Use Only

“The magic of Sherbourne lies not in replacing human creativity, but in creating the rigorous framework within which it can flourish. Our technical choices prioritize both artistic integrity and engineering excellence.” - Engineering Manifesto