Built in Memory and Feedback

Built in Memory and Feedback
Prompt and Chat memory so you don’t have to deal with it.
October 28, 2023
Gopi Krishna Lakkepuram

The absence of built-in memory and feedback mechanisms is awidely acknowledged limitation of Large Language Models (LLMs). This oftenresults in disjointed interactions and misses the valuable learning that comesfrom iterative feedback. HyperleapAI transcends this challenge by offering apowerful feature set that includes built-in memory and feedback for both promptsand conversations. The end result is a seamlessly integrated, self-improving AIexperience that spares you the hassle of memory and feedback management. 

Memory-Laden Prompts and Conversations:Never Worry about Remembering Again

Imagine the power of having your AI system remember past prompt runs and conversations so you don’t have to worry about them yourself. HyperleapAI, comes with a built-in data store so you can focus on running the prompts or conducting conversations and leave everything else to us. The advanced and comprehensive API lets you get any data, or part of it with ease for integrating into your business apps and processes.

Feedback Loops: Continuous improvement

HyperleapAI’s feedback mechanisms are game changers becauseof how they work together with prompt runs and conversations. The system allowscapturing end-user feedback for any prompt run or conversational message, witha numerical score, and text feedback. You could use this feedback to fine tuneyour prompts or train a fine-tuned model, so you can serve better responses toyour users over time.

Standardized Chat Memory: One Conversation,Any AI.

The typical LLMs' lack of memory often leads to fragmented,disjointed conversations. But tying up with a specific LLM creates stickinessif you want to move to a different system later. HyperleapAI's built-in chatmemory is standardized to deal with any LLM that is working behind the scenesto serve responses. So, in the future, if you must switch LLMs, we’ve got youcovered.

Custom Headers: Linking with InternalSystems

Every API call for a prompt run and conversation can send inCustom Headers that we pass through back as a response. This allows you tosafely link internal systems with HyperleapAI, without having to divulge anydetails that are confidential. To learn more about Custom Headers and how theywork, click here.


By leveraging HyperleapAI's built-in memory and feedback capabilities, you’re not just eliminating bottlenecks—you're unlocking a level of AI sophistication and utility that would be otherwise unreachable.

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