Atatürk Conversational AI System
Persona-controlled historical dialogue system using retrieval-augmented generation and advanced prompt engineering.
Download on Google Play StoreMotivation & Problem
Large language models are capable of fluent dialogue, but they tend to hallucinate, drift in persona, or mix modern opinions with historical narratives. This becomes especially problematic when representing real historical figures, where accuracy and contextual integrity matter.
The goal of this project was to explore whether careful system prompt design, retrieval augmentation, and structured reasoning could enable historically grounded conversations—while explicitly limiting speculation, anachronisms, and unsupported claims.
Design Goals
- •Preserve a consistent historical persona
- •Ground responses in verifiable sources
- •Prevent speculation beyond documented material
- •Keep the system transparent about uncertainty
System Overview
Rather than relying on free-form generation, the system emphasizes constraint and grounding as first-class design principles.
User Input
The user asks a natural-language question in an open conversational format.
Context & Control
Relevant historical context is retrieved from curated sources and injected into a structured system prompt that enforces persona and behavioral constraints.
Response Generation
The language model generates a response conditioned on both retrieved context and strict system-level rules.
Retrieval-Augmented Generation
To reduce hallucination and improve factual grounding, the system uses retrieval-augmented generation. User queries trigger a search over a curated corpus of historical texts, speeches, and writings attributed to or directly related to Atatürk.
RAG Components
- •Curated, domain-specific source set
- •Retrieval before generation, not after
- •Context window limited to relevant excerpts
- •Clear separation between retrieved text and model reasoning
Agentic Workflows
For more complex interactions, the system can be orchestrated as a multi-step agentic workflow. Instead of a single prompt-response cycle, intermediate steps retrieve context, refine constraints, and only then generate a final answer.
This design mirrors how a human researcher might consult sources before answering, rather than responding immediately.
Workflow Features
- •Modular agent steps for retrieval, validation, and generation
- •Orchestration via workflow automation (e.g., n8n)
- •Deterministic control flow for reproducibility
Technology Stack
- •Large Language Model: GPT-4
- •Prompt Engineering: System prompts, few-shot examples, constrained reasoning
- •Retrieval: Vector-based semantic search
- •Orchestration: Agentic workflows (e.g., n8n)
- •Frontend: Web-based chat interface
- •Deployment: Containerized services for reproducibility
Limitations & Ethical Considerations
Despite strict controls, no conversational AI can fully replicate a historical individual. This system does not claim authenticity or authority; it is an exploratory interface for engaging with historical material.
Ethical considerations include avoiding political manipulation, ensuring transparency about AI limitations, and clearly communicating that responses are generated interpretations grounded in sources—not direct quotations unless explicitly stated.
Considerations
- •Risk of over-trust in generated responses
- •Cultural and political sensitivity
- •Need for clear user disclaimers
Motivation & Problem
Large language models are capable of fluent dialogue, but they tend to hallucinate, drift in persona, or mix modern opinions with historical narratives. This becomes especially problematic when representing real historical figures, where accuracy and contextual integrity matter.
The goal of this project was to explore whether careful system prompt design, retrieval augmentation, and structured reasoning could enable historically grounded conversations—while explicitly limiting speculation, anachronisms, and unsupported claims.
Design Goals
- •Preserve a consistent historical persona
- •Ground responses in verifiable sources
- •Prevent speculation beyond documented material
- •Keep the system transparent about uncertainty
System Overview
Rather than relying on free-form generation, the system emphasizes constraint and grounding as first-class design principles.
User Input
The user asks a natural-language question in an open conversational format.
Context & Control
Relevant historical context is retrieved from curated sources and injected into a structured system prompt that enforces persona and behavioral constraints.
Response Generation
The language model generates a response conditioned on both retrieved context and strict system-level rules.
Limitations & Next Steps
Despite strict controls, no conversational AI can fully replicate a historical individual. This system does not claim authenticity or authority; it is an exploratory interface for engaging with historical material.
Next Steps
- •Explicit citation of sources in responses
- •Multi-language support
- •Comparison between constrained vs. unconstrained prompts
- •Evaluation with historians or domain experts
- •Extension to other historical figures using the same framework
Retrieval-Augmented Generation
To reduce hallucination and improve factual grounding, the system uses retrieval-augmented generation. User queries trigger a search over a curated corpus of historical texts, speeches, and writings attributed to or directly related to Atatürk.
Key Features
- •Curated, domain-specific source set
- •Retrieval before generation, not after
- •Context window limited to relevant excerpts
- •Clear separation between retrieved text and model reasoning
Agentic Workflows
For more complex interactions, the system can be orchestrated as a multi-step agentic workflow. Instead of a single prompt-response cycle, intermediate steps retrieve context, refine constraints, and only then generate a final answer.
This design mirrors how a human researcher might consult sources before answering, rather than responding immediately.
Workflow Features
- •Modular agent steps for retrieval, validation, and generation
- •Orchestration via workflow automation (e.g., n8n)
- •Deterministic control flow for reproducibility
Technology Stack
- •Large Language Model: GPT-4
- •Prompt Engineering: System prompts, few-shot examples, constrained reasoning
- •Retrieval: Vector-based semantic search
- •Orchestration: Agentic workflows (e.g., n8n)
- •Frontend: Web-based chat interface
- •Deployment: Containerized services for reproducibility
Limitations & Ethical Considerations
Despite strict controls, no conversational AI can fully replicate a historical individual. This system does not claim authenticity or authority; it is an exploratory interface for engaging with historical material.
Ethical considerations include avoiding political manipulation, ensuring transparency about AI limitations, and clearly communicating that responses are generated interpretations grounded in sources—not direct quotations unless explicitly stated.
Key Considerations
- •Risk of over-trust in generated responses
- •Cultural and political sensitivity
- •Need for clear user disclaimers
Implementation Challenges
- •Maintaining consistent persona across multiple conversation turns
- •Handling ambiguous or incomplete historical sources
- •Preventing hallucination and anachronistic responses
- •Balancing persona control with natural conversational flow
Future Improvements
- •Explicit citation of sources in responses
- •Multi-language support
- •Comparison between constrained vs. unconstrained prompts
- •Evaluation with historians or domain experts
- •Extension to other historical figures using the same framework