Despite the remarkable capabilities of advanced language models, there are instances where the generated content may contain inaccuracies or deviate from the intended context. This occurrence is commonly referred to as "hallucination,. This article outlines effective strategies to minimize hallucinations, thereby enhancing the reliability of Lampi AI's outputs.
Strategies for minimizing hallucinations
Allow your AI assistant to express uncertainty
Empowering your assistant to acknowledge when it does not have sufficient information can significantly diminish the likelihood of producing misleading content. By permitting this admission, you can encourage more cautious and accurate responses.
As our financial analyst, please evaluate this investment proposal for XYZ Corp.
{{PROPOSAL}}
Instructions: Focus on key financial indicators, potential risks, and market trends. If you encounter any ambiguous areas or lack of data, respond with, "I cannot adequately assess this without more information."
Utilize direct quotes for factual verification
Instruct your assistant to extract verbatim quotes before proceeding with any analysis. This approach helps ground its responses in the original text, minimizing the potential for hallucinations.
Review this compliance document for adherence to relevant regulations.
{{COMPLIANCE_DOCUMENT}}
Your task is to extract the most relevant quotes pertaining to regulatory compliance. If no pertinent quotes are found, state, "No applicable quotes found."
Utilize the extracted quotes to evaluate compliance, referencing each quote by its number. Ensure that your assessment is founded solely on the provided quotes.
You can also require your assistant to cite relevant quotes and sources for each assertion made.
Other techniques
- Restrict the Assistant to use only provided knowledge: Explicitly guide your assistant to rely exclusively on the provided documents and contexts, and refrain from drawing on its general knowledge base.
- Step-by-Step reasoning: Encourage your assistant to articulate its reasoning process in a step-by-step manner before arriving at a conclusion. This practice can help uncover any logical flaws or incorrect assumptions.
- Multiple output comparison: Run the same prompt through your assistant multiple times and compare the results. Discrepancies among the outputs may indicate potential hallucinations.
Note: While these strategies can significantly reduce the occurrence of hallucinations, it is important to recognize that they cannot be completely eradicated. Always verify critical information, especially when making significant decisions that could have substantial implications. Adopting these best practices will enhance the accuracy and reliability of your AI-generated outputs.
Verify AI outputs with Lampi
Lampi has integrated a cutting-edge verification system to facilitate the assessment of AI-generated outputs when utilizing the Search and Web tools. This system is designed to enhance the trustworthiness of the information provided and ensure users can easily cross-reference the content with original sources.
Accessing source files
When you submit a query, Lampi first displays all the files utilized to generate the response. You can click on any of these files to view them in detail, allowing you to examine the relevant sections that informed the AI's answer. This feature enables users to verify the context and accuracy of the information presented.
Source attribution for each sentence
For each sentence generated by Lampi, the system identifies the exact sources of information used to construct that statement. You have the option to view the specific excerpts from the relevant texts. This transparency helps you understand the foundation of the AI's insights and ensures that the content is rooted in reliable sources.
Warning: Lampi strives to highlight the most pertinent passages in the snippets provided. However, there may be instances where the displayed text does not represent the exact wording. The AI may have integrated various parts (or chunks) of the source material to form a single insight, leading to the display of only one segment.