Practical Ways to Stop Models from Hallucinating
One of the major hurdles in implementing LLM models in a real business environment is their tendency to hallucinate.
Generative AI is everywhere. We all want to use it to make things work smoother, better, and faster. But one of the major hurdles in implementing LLM models in a real business environment is their tendency to hallucinate.
Not so long ago, "hallucination" referred to vivid dreams or imaginative visions. Now, it describes AI models generating responses that sound convincing but are factually incorrect. The term even made it as Dictionary.com's word of the year in 2023. These AI "hallucinations" can be a real headache, but don’t worry—there are some strategies to help limit them and keep your AI outputs more reliable.
Understanding Hallucinations
Imagine asking for directions and being sent on a wild goose chase. That’s what happens when AI hallucinates: it provides plausible-sounding but incorrect information. For some use cases that require more creativity, this won’t be a great issue, but In marketing and e-commerce tasks and research where precision is key, this can lead to misguided strategies and wasted resources.
In a preprint study released in January, three machine-learning researchers at the National University of Singapore presented a proof that hallucination is inevitable in large language models.
Limitations of LLM Models
It's crucial to remember that LLMs are bounded by the data they were trained on. For example, a model trained up to 2021 won’t have insights beyond that year. This can lead to potentially outdated responses.
Although hallucinations are inevitable there are some strategies that will help you limit them.
Strategies to Minimise Hallucinations
Be Clear and Specific: Vague questions yield vague answers. Instead of "What’s the best marketing strategy for beauty industry?" try "What are three proven marketing strategies to boost online sales in the beauty sector? Please base your answer only on the proven information you got.”
Ground Your Questions in Facts: Use concrete data to frame your questions. For instance, "Based on the 2023 e-commerce trends report, what strategies should beauty brands adopt to boost sales?"
Double-Check the AI’s Work: Always cross-verify AI responses with trusted sources. It's like fact-checking an article before it goes to print.
Refine and Retry: If the AI’s answer seems off, tweak your prompt and ask again. Sometimes a slight adjustment can make a world of difference. Be careful though: sometimes models may be too willing to make you feel good and accept 100% of your corrections even if you are wrong.
Leverage RAG (Retrieval-Augmented Generation): This technique combines the AI’s generative power with real-time data retrieval, grounding responses in current documents and reports.
Example: Ask about the latest SEO strategies, and a RAG model will fetch recent articles and insights before crafting a response.
Tools: Platforms like Perplexity.ai use RAG to provide accurate, contextually relevant answers, greatly reducing the risk of hallucinations.
Importance of Limiting Hallucinations in Marketing
In the marketing world, decisions must be data-driven. Hallucinations can lead to misinformation, flawed strategies, and ultimately, lost trust and revenue. Imagine launching a campaign based on an AI’s misguided trend prediction—it could send your efforts (and budget) spiraling in the wrong direction.
Accurate AI outputs are crucial for:
Developing Solid Strategies: Building marketing plans on verified, reliable data.
Maintaining Trust: Ensuring clients and customers receive accurate information.
Efficient Resource Allocation: Directing efforts and budgets toward strategies that work.
Practical Example
Original Prompt: "What are the latest trends in digital marketing?"
Refined Prompt: "Based on recent industry reports, what are the top three digital marketing trends for 2024 in social media and content marketing?"
By applying these strategies, including the use of RAG and tools like Perplexity.ai, you can significantly cut down on AI hallucinations, ensuring your digital marketing efforts are both innovative and accurate. This not only enhances the reliability of your campaigns but also strengthens your brand’s credibility in the ever-competitive digital landscape.