I’m working on a project that uses the Assistant API, and I’ve encountered the challenge of hallucinations — instances where the API generates convincing but incorrect or unverifiable fact-based responses. I would like to know if anyone has experience or recommended strategies to minimize this behavior.
In particular, I’m interested in:
Validation and Verification Strategies: Once I have a generated response, how can I automate the verification of its accuracy? Has anyone implemented effective fact-checking systems or post-processing techniques?
Use of Complementary Technologies: Are there any additional tools or APIs you would recommend to help validate or enrich the responses generated by the API?
Any advice, shared experiences, or resources you could recommend would be greatly appreciated. I am particularly interested in practical solutions that have been proven effective in real projects.
There’s no clear-cut way to handle verification, and since you’re using the API, you have quite a bit of choices.
Right off the bat, this company seems like it has verification capabilities.
I wonder too, and this is just a thought, if you could leverage another API call that takes information from a website and compares it to the LLM’s original output to determine accuracy. However, it’s likely not quite that simple.