> For the complete documentation index, see [llms.txt](https://docs.humanchain.network/whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.humanchain.network/whitepaper/fundamentals/3.-applications/3.6-llm-wrappers.md).

# 3.6 LLM Wrappers

Not just limited to LLMs but any AI model will have an interface and the interface needs to ensure that certain inputs such as financial information, personally identifiable sensitive information, prompts which are a direct violation of regulations, etc. are not sent to the LLM or GenAI models. \
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On the other hand, companies need to ensure that outputs that are highly sensitive or classified or secure information aren't shown or disclosed to users such as nuclear capabilities, synthetic biology, virology, etc. \
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A safe and compliant LLM or GenAI wrapper could leverage HumanChain for prompt detection, prompt enhancement, data privacy preservation, model training, response sanitization, and constant updation of sensitive input prompts or sequences of prompts.

**How it works:**

1. **Standardization:** Wrappers provide a consistent interface for interacting with various LLMs, regardless of their underlying architecture or API. This allows developers to use different models without rewriting the code.
2. **Abstraction:** They handle low-level communication details with the LLM, like authentication and API calls. Developers can focus on crafting prompts and processing responses.
3. **Enhanced Functionality:** Wrappers can offer additional features on top of the core LLM functionality. These might include:

* Conversation Management: Tracking conversation history for context-aware responses in chatbots.
* Memory Management: Storing and retrieving past interactions for a more coherent user experience.
* Prompt Engineering: Assisting with crafting effective prompts to get the desired output from the LLM.

**Value propositions:** 

1. **Data Protection:** Wrappers can effectively filter sensitive information like financial data, PII, or trade secrets from being sent to the LLM, protecting user privacy and company assets.
2. **Compliance Adherence:** They can be programmed to prevent prompts or outputs that violate regulations, such as those related to discrimination, hate speech, or copyright infringement.
3. **Output Filtering:** Wrappers can filter sensitive or confidential information generated by the LLM, preventing accidental disclosure.
4. **Model Guidance:** They can provide additional context or constraints to the LLM, improving the quality and relevance of outputs.
5. **Performance Optimization:** By managing input and output, wrappers can help optimize LLM performance and reduce costs.


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