Accuracy and Reliability of AI Agent Responses

At Patsnap, we are always working on making improvements to make sure the accuracy of the results our customers receive is of the highest possible standard and can be relied upon. This is no different with our AI agent features especially considering the preconception of LLMs that they are built based upon. Within this article, we will answer some common questions around the accuracy and reliability of our AI agents.

Where do the results produced come from and how reliable are they?

For each AI agent, we will first search within the relevant data sets (e.g. patents, papers, etc.). These results will then be reranked and transferred to our LLM which will then summarize with the final output that you see produced.

Why do hallucinations occur?

Hallucinations for LLMs refers to when inaccurate information is produced based on a misunderstanding by the LLM of the underlying data. It is impossible to completely remove all hallucinations for any LLM given that it is ultimately AI performing interpretations, however we employ RAG (Retrieval-Augmented Generation) within our LLM to significantly reduce the amount of hallucinations.

Are improvements consistently being made to the quality of results?

Yes, we are continuously making improvements and iterations to our LLM to make sure you get back the highest quality responses. In addition, we are always improving the quality and quantity of our data which underlines what our LLM is interpreting.

Is there anything I can do to improve the accuracy of results?

Even though we are continually improving the quality of the responses provided by our LLM, there are some techniques you can employ within your questionning to further increase the accuracy:

1. Make your question as specific as possible and request the agent to provide steps in it's thinking and to cite the data sources for the answers provided.
2. Provide sufficient context with supplementary background information. By definition AI is not in the real world, so any additional key information provided will reduce the potential for the agent to try to guess answers.
3. Break down complex questions, or in other words decompose multiple tasks into single tasks for the agent to consider.