AI Issues: Handling Hallucinations and Prompt Drift

The DAS crew kicked off the podcast by defining hallucinations - when large language models like ChatGPT convincingly provide false information. They shared amusing anecdotes of AI assistants like Claude and Pi continuing to insist they could complete impossible tasks.

The key reasons behind hallucinations were discussed:

• AI models work based on predicting the most probable response, not necessarily factual accuracy. They don't actually "know" if their responses are right or wrong.
• Even when facts may be present in the model's training data, it can still provide incorrect information. This demonstrates the limitations of current AI.
• Ambiguous prompts can lead models to guess and hallucinate more. Being ultra specific with prompts can help reduce this.
• The "temperature" setting also impacts creativity vs. accuracy. Lower temperatures lead to less hallucination risk.

The hosts then covered prompt drift - when model responses veer off the initial prompt topic. Reasons discussed:

• Limits to thread memory in conversations
• Model architecture changes between versions
• Ambiguity in prompts

Breaking prompts into smaller, simpler pieces can help reduce drift

Continuously evaluating production prompts is key to catch drift

Consider both short term drift in conversations and long term drift in automated systems.

The overarching advice was keeping prompts simple, specific, and continuously evaluated as key to reducing harmful hallucinations and prompt drift.