I Built a Meta Prompt That Actually Fixes GPT’s Hallucination Problem

Addressing GPT Hallucinations: Introducing the VERITAS Framework for Reliable AI Responses

In recent months, many users have noticed a decline in the reliability and accuracy of AI language models like ChatGPT, especially following updates such as GPT-5 and the removal of previous models. A common challenge has been the persistent issue of hallucinations—instances where the model fabricates facts, provides unverified information, or responds unpredictably. This has understandably led to frustration among developers, researchers, and everyday users seeking dependable AI interactions.

Developing a Solution: The Birth of VERITAS

Motivated by these persistent issues, I undertook a project to design a framework that could significantly mitigate hallucinations in GPT outputs. Drawing on advanced prompting techniques, the Graph of Thought reasoning paradigm, and anti-hallucination strategies, I developed a structured approach named VERITAS. The goal was to create a system that encourages transparency, verification, and accountability in AI responses.

Key Features of the VERITAS Framework

• Source Citation Enforcement: Ensures GPT cites credible sources for all factual information.
• Fact Verification: If the model cannot verify a statement, it transparently indicates so rather than making unsupported claims.
• “BIZZARO” Audit Command: Provides an internal review mechanism that scans responses for inaccuracies or hallucinations.
• Automatic Prompt Enhancement: Dynamically refines prompts to improve response quality based on ongoing interactions.
• Comprehensive Chat Logging: Saves full dialogues for further analysis or download.
• Recursive Quality Checks: Implements iterative verification processes to progressively enhance response reliability.

How to Implement VERITAS

The framework is designed to be integrated seamlessly as a system prompt in your GPT interactions. Once set, it provides immediate feedback by confirming with the phrase “VERITAS ENGAGED,” signaling that the safety and verification protocols are active. From there, the model’s responses include detailed verification statuses, confidence scores, and source attributions. This structure fosters a more trustworthy and accountable AI experience.

Performance and Results

After a week of rigorous testing, the impact of VERITAS has been remarkable. The AI responses are now consistently more accurate, sourced, and resistant to hallucinations. The framework’s layered verification approach helps produce responses that users can rely on, restoring confidence in AI-assisted workflows.

Technical Details

Designed with efficiency and compliance in mind, the entire framework comprises under 3,000 characters—making it suitable for most prompt