Did ChatGPT piss you off by not giving you the right answer?
LLM models very often give incorrect or misleading information despite the fact
that the LLM has the information available. Why is that?
AI Is Only As Good As Your Data
Crap in, crap out.
It always depends on the quality of the input data from which the dataset for AI agents is created. If the input data is highly structured or lacks context, the responses will be poor too.
WHAT'S CAUSING THE PROBLEM?
Automated data processing.
Most other GPT-based solutions process the data automatically and thus handle the errors that the input data contains. If the dataset contains errors, the quality of the answers decreases again.
Context is the Key
Don't forget about the context.
Most competing AI tools do not take the structure of the content into account when processing information. As a result, AI agents may lack the context necessary to generate a correct and accurate response.
Vector databases aren't enough
Don't rely on a vector database.
All traditional tools that use GPT as knowledge assistants use a vector database to store domain-specific information. However, it does not have enough context and therefore the bot's answers are often inaccurate.
Accuracy is the King!
Kaila offers the market-leading 95% accuracy of answers based on the given dataset.
Your data matters to us.
We process the input data automatically, but our team then cleans the dataset to fill in missing context that is important for quality and accurate responses.
What makes us stand out
Thanks to our unique Neural Graph technology that significantly improves the accuracy of answers from our KaiLLM based on an embedded external dataset you can increase both the understanding of the user's query and the quality of the answer.
Why we are achieving the best results
Neural Graph technology.
We've developed additional AI layer on top of the LLM. The Neural Graph links related data to capture meaning and relationships, providing the context that traditional vector databases lack. Kaila combines knowledge graphs' connectivity with vector databases' scalability.