Understanding how AI generates responses is crucial. AI models predict and generate outputs based on patterns in the data they were trained on, not through independent reasoning or human-like cognition.
AI-generated content is not always 100% reliable and should be critically evaluated. Generative AI is learning as well…
AI Flaws and Limitations
- AI Hallucinations – AI may generate incorrect or completely fabricated information. It has been notoriously known for creating non-existent resources to cite from.
- AI Biases – AI reflects biases present in its training data, leading to skewed outputs.
- Lack of Contextual Understanding – AI cannot fully comprehend nuanced or ambiguous queries. Its inability to understand the complete picture because of limited training data leads to incorrect tokens being recalled to fill in the gap.
- Inconsistencies in Responses – AI-generated answers may vary even with similar prompts.
To ensure the accuracy and reliability of AI-generated content we must always
- Break Down Responses – Dissect complex AI outputs to check for inconsistencies. Try understand each part and where it came from. Can you easily explain it to someone else or is it too ambitious?
- Refine Prompts Continuously – Modify inputs based on responses to improve output quality. Double check if similar prompts are providing different responses.
- Cross-Check with Credible Sources – AI can produce inaccurate or misleading content. Always check a search engine to find another source or ask an expert.
- Be Aware – AI is not all knowing and keeping that at the back of our mind is always important so we do not get lost in the facade.