One critical weakness of Large Language Models (LLMs) is their inability to continue learning after completing their training. They operate on static, pre-trained data, and while these models can generate human-like text, they often fail to incorporate the latest information, leading to inaccuracies or irrelevant content.
Retrieval-Augmented Generation (RAG) systems break this barrier by bringing new, supplemental data into prompts and workflows through search technologies.
In market research, timely and personalized information is crucial, and the combination of LLMs and RAG is revolutionizing how we conduct synthetic research. This system combines the creativity of language models with up-to-date, hyper-relevant data, ensuring the most context-aware and accurate responses.
What is RAG?
RAG utilizes two key components:
The Retriever: The retriever searches through massive databases, locating the most relevant pieces of information for a given query. In Invisibly’s case, the real-life responses from our users populate a knowledge base that informs said query.
The Generator: Once the data is retrieved, it is provided to state-of-the-art LLMs to function as the generator, crafting relevant responses that are both nuanced and accurate.
RAG at Invisibly
Whenever a Synthetic Persona answers a survey, our model retrieves the most relevant prior data about this Persona and similar Human Personas, leveraging the fresh data powered by the users of our mobile app. The AI workflow can then deliver precise and relevant answers. This fusion between retrieval and generation is essential for our Synthetic Audience, which strives to mimic real-world user behavior with high fidelity.
Why RAG is Essential
1. Real-Time Data Accuracy
Invisibly’s Synthetic Audience aims to deliver real-world insights as accurately as possible. By tapping into RAG, our Synthetic Audience consistently pulls in fresh, relevant information, making its insights reflective of the latest trends and behaviors, as reported by our human survey respondents. This real-time precision makes Invisibly’s research far more reliable than traditional LLMs that rely on outdated data sets.
2. RAG Dynamically Evolves
One of the key advantages of RAG models is adaptive learning. As data flows into the system, RAG models dynamically adjust their understanding, ensuring that responses evolve alongside changing market conditions or consumer sentiments. This level of contextual depth allows our panels to provide more detailed and nuanced insights, whether you’re looking to understand customer preferences or track the consumer sentiment of a new product launch.
3. Cost Efficiency
Traditional research methods—whether manual data collection or large-scale surveys—are time-consuming and costly. RAG helps Invisibly do this instantly and with far greater detail than any LLM, providing world-class insights at a fraction of the cost. By integrating RAG, we deliver 10x more efficient research solutions, making advanced market insights accessible to businesses of all sizes.
Enhancing User Trust and Data Integrity
Traditional AI systems often generate “hallucinated” data—information that sounds plausible but is factually incorrect. RAG avoids this by linking every response to the external data it retrieved, allowing users to verify the sources of its answers.
Traditional AI systems also generate highly “average” responses based on the content that appears most in their training data.
RAG avoids this trap of responding only with mainstream knowledge and stereotypes by explicitly navigating to the content areas dictated by our statistically significant, human-by-human data.
This transparency is particularly crucial for our research, which promises to deliver trusted, accurate insights. RAG allows us to ensure the factual correctness of our outputs and reinforces our commitment to providing reliable data to our partners.
Synthetic Research is The Future
RAG on the Invisibly platform enables a fundamental shift in how businesses can leverage real-time data to gain insights. For our market research solutions, RAG helps us deliver instant, reliable, and cost-effective insights, helping companies confidently navigate the massive amounts of information required when conducting research.
As RAG continues to evolve, it will unlock even greater possibilities for Invisibly’s Synthetic Audience, making it the go-to platform for businesses seeking unbiased, data-driven insights at unmatched speed and scale.
Dave is Invisibly’s Chief AI Officer, spearheading the company’s gen-AI research platform. He holds degrees in Mechanical Engineering and Philosophy-Neuroscience-Psychology from Washington University in St. Louis, along with an MBA from St. Louis University. His expertise spans AI, machine learning, natural language processing, data science, and product development, driving innovation in our synthetic data solutions.
