Product insights & monitoring, testing, end-to-end analytics, and errors are four of the most difficult LLMs to monitor and test. Teams mostly waste weeks of dev time building internal tools to solve these problems. Most product analytics efforts have concentrated on numerical metrics like CTR and conversion rates. This information is critical, yet it is incomplete. Contrarily, text data offers a more comprehensive comprehension of user sentiment and behavior. But it’s not always easy to analyze text data.
Meet Lytix, the LLM stack enhancer that integrates testing, insights, and end-to-end analytics with little coding modifications. Lytix has developed an all-inclusive platform for analyzing text data in response to these difficulties. Lytix automatically mines text data for insights using natural language processing techniques, such as:
- Through sentiment analysis, Lytix can determine the tone of text data, including whether it is favorable, negative, or neutral. Gaining insight into client happiness, pinpointing product issues, and measuring marketing campaign effectiveness can all be facilitated by this.
- Lytix can extract the most important themes from text data through topic modeling. Insight into client wants and needs, new trend detection, and product opportunity discovery can all benefit from this.
- Lytix can recognize entities in text data, such as persons, places, and things. Customer demographics, typical use cases, and mentions of competitors can all be better understood with this information.
Here’s how Lytix assists with YC-bot deployment and performance tracking in production:
Keeping expenses low
Lytix was concerned about the cost per call as the pipeline contains multiple hefty LLM calls. Lytix always went with the least expensive LLM provider (rather than the fastest, most dependable, etc.) using OptiModel because money was their top concern. Avoiding the trouble of creating unique codes for every supplier contributed to a 1/3 reduction in LLM expenses.
Identifying errors
Wherever you throw an error, use the new Lytix LError class. The main objective of this Lytix is to inquire about the user’s business and application-specific details. Because of this, similarity has become a key statistic to monitor. Lytix set up a custom alert so that Lytix-bot would send a Slack message if it detected that the model’s question did not adequately match the given context.
Also, on the Lytix dashboard, you may specify which “themes” you’d like the app to use to categorize your sessions. If an intent is not defined, Lytix automatically tags sessions with the intent that best describes them. You can always re-configure your themes or look into past sessions to alter their visibility in your analytics stack.
In Conclusion
Lytix integrates with your LLM stack to provide insights, testing, and end-to-end analytics while requiring minimal code modifications.
Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world making everyone's life easy.
