Input
Receive an open natural-language question from the user.
Generative AIPrompt EngineeringRAGAgentic WorkflowsLLM Systems
Historical Conversational AI System
Source-aware historical dialogue using retrieval and persona constraints
A conversational AI product designed to provide historically grounded interaction through curated retrieval, explicit persona boundaries and controlled generation workflows.
Open project
Motivation & Problem
Language models can produce fluent historical dialogue while still introducing unsupported claims, anachronisms or inconsistent persona behavior.
This project evaluates a controlled workflow in which retrieved context and explicit behavioral constraints are treated as first-class system components.
Design goals
- Preserve a stable historical persona
- Ground responses in curated source material
- Limit unsupported speculation
- Communicate uncertainty clearly
System Overview
The workflow separates user input, contextual grounding and final response generation to make failures easier to identify and correct.
Context & Control
Retrieve relevant passages and apply persona, scope and style constraints.
Generation
Produce a response conditioned on both the source context and behavioral rules.
Architecture & Flow
1. User-query preprocessing 2. Retrieval of relevant historical passages 3. Assembly of persona and behavioral constraints 4. Language-model response generation 5. Post-generation style and scope checks
Prompt & Persona Design
The system prompt defines role, temporal boundaries, response style and uncertainty behavior. The model is instructed to avoid unsupported extrapolation and to remain transparent when the source material is insufficient.
Retrieval-Augmented Generation
A curated corpus is searched before generation so that relevant historical context can be injected into the model input.
RAG components
- Domain-specific source collection
- Retrieval before response generation
- Context limited to relevant excerpts
- Clear separation between source text and generated interpretation
Agentic Workflows
More complex requests can be processed as a deterministic multi-step workflow that retrieves, validates and then generates a final response.
Workflow features
- Modular retrieval, validation and generation steps
- Workflow orchestration with n8n
- Reproducible control flow and logging
Technology Stack
Components
- GPT-4 language model
- System prompts and constrained examples
- Vector-based semantic retrieval
- n8n workflow orchestration
- Web-based chat interface
- Containerized services
Limitations & Ethical Considerations
The application does not claim to reproduce or speak with the authority of a historical individual. It is an exploratory interface for engaging with historical material.
Generated interpretations must remain clearly distinguishable from direct quotations and verified primary-source content.
Key considerations
- Risk of over-trust
- Cultural and political sensitivity
- Need for visible AI and source limitations
Future Extensions
Planned improvements
- Source citations in responses
- Multi-language support
- Evaluation with historians and domain experts
- Reusable framework for additional historical figures