AI is transforming how businesses bid for and win new work. From dramatically increasing efficiency to boosting win rates and retaining talent, the possibilities promised by AI are endless. However, as with any new technology, AI also presents new challenges and has limitations that need careful consideration. 

In this article, we explore the topic of AI content reliability and discuss how AutogenAI is pioneering efforts to enhance the reliability and accuracy of AI-generated content, setting new standards in the industry.  

 

Challenges in the Reliability of AI-Generated Content

AI-generated content is everywhere, but can you always trust it? Not entirely. While AI can create content at an incredible speed, it is not always accurate. 

Although Large Language Models (LLMs) can generate coherent text on many topics, they may occasionally produce factual errors or “hallucinations”. In other words, they often confidently state things as fact that are actually incorrect or just completely untrue. 

The reality is, AI is not a truth telling machine. In many respects, it’s like very sophisticated predictive text. It is designed to predict the next word in a sequence and write eloquently. It’s not designed to be factual. 

The reliability of AI content also varies depending on the large language model being used, and the training dataset of that LLM. The accuracy of AI content often hinges on how recently the model has been updated, as older models might produce content based on outdated information. Additionally, AI can struggle with nuanced reasoning and may misinterpret context or intent. 

 

How AutogenAI is Solving this problem 

The AutogenAI team began thinking about how to solve ‘hallucinations’ as early as 2021 – long before AI hallucinations were being regularly talked about in the media. In fact, in those days we had our own name for AI hallucinations based on the style of a very famous politician (but we can’t share that name here). 

 

Our  AI and linguistic engineering teams said there were two ways of solving this problem. We could either: 

1. Define ‘Truth’ very clearly, and then teach this definition to the large language models; or 

2. Make large language models provide references and proof for the content they generate and teach them to prioritize using accurate and factual information from knowledge libraries over other sources when the answer requires it.  

Considering option 1 is something people have been trying to do since the dawn of civilization (and failing at), we opted for option 2. 

AutogenAI significantly enhances the accuracy and reliability of AI-generated content through advanced ‘grounding’ techniques which include multi-agent workflows, task decomposition, linguistic engineering, and Retrieval-Augmented Generation (RAG). 

These deeply technical methods enable AI models to reference and cite data from an enterprise’s knowledge base or document library or the internet. They quantifiably reduce errors and enhance factual accuracy. 

 As pioneers in these methods, AutogenAI has established a robust framework for improving the reliability of AI-generated content.  

Using these approaches, AutogenAI has strengthened its commitment to advancing AI reliability. These approaches not only help to bridge the gap between the model’s training data and the accurate, relevant information needed in bid responses but also ensure that the content produced is both reliable and trustworthy. 

 

The Importance of Human-in-the-Loop Processes

When users perform text transformations on AutogenAI, the tool always generates multiple options for the user to choose from. This is one of the methods we deliberately use to guarantee human interaction and intervention at every stage of content generation. 

Although our tool has solved for hallucinations, we still believe that ‘human-in-the-loop’ processes combined with ‘statement-of-fact‘ verification are crucial for ensuring the reliability of AI content. AI is very good, but human oversight at each step of content generation allows for fact-checking, contextual understanding, and quality control that AI alone cannot provide. Humans can verify sources, catch errors, and add nuanced perspectives that enhance the overall reliability of AI-generated content.  

Our tool continuously learns from each human interaction to better produce reliable and relevant content. 

While AI content generation has made significant strides, its reliability still requires careful consideration. AutogenAI’s pioneering use of advanced grounding techniques like those discussed above, combined with human-in-the-loop processes, offers the most promising approach to producing accurate, trustworthy AI-assistant generated content quickly. 

 

To learn more about how AutogenAI can transform your bid and proposal writing process, contact us today.