by Kalpan Vaghela, on Mar 13, 2025 2:59:34 PM
Key takeaways from the blog
- Copilot-driven solutions hallucinate at times depending on the underpinning training models.
- The underlying AI-models should have high-quality training data, which is enriched with continuous feedback loops.
- Businesses should strategically select mega data sets or niche data sets as per the business requirement.
Automation solutions having AI-powered conversational interfaces, such as Copilots, tend to hallucinate at times. The phenomenon mainly occurs when the prompt questions are not crisp, direct, and to-the-point. Lengthy and ambiguous prompts almost always pose the risk of inaccurate responses or hallucinations. Only a knowledgeable and well-informed person in the particular domain can identify and isolate such instances of AI-hallucinations. However, data sanctity and data reliability issues lie at the very core of the hallucinations. These issues need to be handled right at the inception of the AI-models. Otherwise, these issues assume disproportionate importance over time and breed bias. As a result, investments in technology, such as Copilot, that can be harnessed to improve business efficiency and productivity can become futile.
What are the hallucinations in data-driven Copilot solutions?
Copilot solutions or any Large Language Model (LLM) based solutions that are not grounded enough in the enterprise data sometimes make up natural language answers that seem correct but are not factual. The underlying model architecture, inference strategies, and pattern misinterpretation, while generating answers where direct answers are unavailable related to the query and the training data, are the main causes of hallucinations. Copilot solutions powered by more narrowly focused models (often called Small Language Models or SLMs) tend to provide more specific answers. They are probably less prone to hallucinations—but they are not immune altogether. Having said that model fine-tuning and using high-quality data though significantly reduce hallucinations, AI Copilots can throw up inaccuracies at certain times, albeit less frequently. Thus, AI-hallucinations remain an active area of interest and research.
What are the steps to reduce hallucinations from Copilot-driven solutions?
The best option for reducing hallucinations in Conversational Interface solutions, such as Copilots, is training and retraining the reference data in the Language Model using continuous feedback loops. Some of the steps involved are –
- Improve reference or training data: Leverage the Copilot project only with a high-quality dataset. Insist on having clean data that is original and not fabricated or extrapolated. Data-fabrication, though fills the essential fields, results in poor query response. Establish automated validation mechanisms over and above the manual checks to improve data integrity.
- Optimize the AI-models: Collect the natural language feedback from users over a period of time and continuously re-train the Language models. Fine-tune the models periodically to improve the data quality and, hence, the Copilot responses.
- Use reference pages-based manual validation: Leverage links of original source documents along with the probable query answer. Also referred to as citations, use them to provide a logical way to the user to manually validate the reference point in the underpinning knowledge base.
- Deploy user feedback mechanisms: Insist that the users flag the Copilot responses or comment on receiving a Copilot query response with remarks, such as Excellent, Good, and Bad, or a similar numerical scale. This way, the underpinning AI-model knows its performance score and aligns accordingly.
- Use crisp prompts: Solutions with conversational interfaces, such as Copilots, require the right prompts for giving the right answers. Ask to-the-point, pertinent questions so that the AI-model offers the right answer.
- Implement Retrieval-based Augmentation (RAG): Link the AI Copilot solution to verified data sources, in real-time, to further ground the model responses.
Simply put
Copilot solutions automate processes with ease to improve business productivity and efficiency. However, the AI-models and the underpinning training data pose a make-or-break difference. Poorly trained AI-models and insufficient model training data cause AI-hallucinations that render the Copilot investment futile. Businesses have to harness model training and re-training based on high-quality data right from inception to reduce hallucinations.
Disclaimer: While organizations can implement strategies to reduce hallucinations, they might not eliminate them completely. Frontier research in the areas of AI-models and AI-hallucinations, continuously improve the AI-models with better outcomes on each interaction and feedback to the Copilot solution. Continuous training and re-training of the models with real-time feedback loops remain the most acceptable way around AI-hallucinations.
