While guardrails in prompts are useful, a more effective step to prevent AI agents from hallucinating is careful model selection. For instance, using Google's Gemini models, which are noted to hallucinate less, provides a stronger foundational safety layer than relying solely on prompt engineering with more 'creative' models.

Don't assume AI output is inherently correct. An expert at Databricks shared that running the same prompt across the top five AI providers yields distinctly different answers. This proves human oversight is crucial to question, validate, and contextualize AI-generated responses before acting on them.

When your primary AI assistant gets stuck, export the conversation and feed it to a different model (e.g., GPT-4 or Gemini). This 'second opinion' can critique the original interaction and help you revise your prompt to get back on track, rather than trying to argue with the stuck AI.

Create a custom Claude Code skill that sends a spec or problem to multiple LLM APIs (e.g., ChatGPT, Gemini, Grok) simultaneously. This "council of AIs" provides diverse feedback, catching errors or omissions that a single model might miss, leading to more robust plans.

Instead of relying on a single AI, use different models (e.g., ChatGPT for internal context, Claude for an objective view) for the same problem. This multi-model approach generates diverse perspectives and higher-quality strategic outputs.

For complex tasks, don't rely on one AI model. A "model council" approach queries multiple models (e.g., Claude, Gemini, ChatGPT) simultaneously, then synthesizes outputs to show agreement, disagreement, and unique findings for more robust decisions.

Different LLMs have unique strengths and knowledge gaps. Instead of relying on one model, an "LLM Council" approach queries multiple models (e.g., Claude, Gemini) for the same prompt and then uses an agent to aggregate and synthesize the responses into one superior output.

To combat hallucinations and bias, don't rely on a single AI tool. For important decisions, query multiple large language models (e.g., Claude, Gemini) with the same prompt. This "second opinion" approach allows you to compare answers, identify inconsistencies, and blend the best elements for a more reliable outcome.

To move beyond casual use, serious AI practitioners should use and pay for premium versions of multiple models (e.g., ChatGPT, Claude, Gemini). Each model has a different 'persona' and training, providing a diversity of thought in their outputs that is essential for complex tasks and avoiding vendor lock-in.