Gentoro FAQs

Interacting with enterprise systems through natural language involves complex LLM function-calling, API knowledge, function design and implementation, and system expertise. Gentoro simplifies this by automatically generating and managing these functions, saving your developers time.

Given the unpredictable and sensitive nature of GenAI, a minor change to a function to improve accuracy for one prompt can suddenly cause other prompts to hallucinate. Achieving the right balance requires tremendous patient trial and error. Gentoro addresses this by evaluating the entire function set holistically and, if needed, rebuilding it to maintain overall accuracy and reliability.

Gentoro is LLM-agnostic, meaning it works with any Large Language Model. This flexibility allows you to switch between different LLM providers without being locked into one, ensuring your integrations stay adaptable as AI technologies evolve. Additionally, since each LLM has unique behaviors, Gentoro can automatically rebuild the function set to ensure smooth compatibility with any new model you adopt.

Gentoro connects to any API that supports the OpenAPI spec. We add other protocols like GraphQL, Protobuf, etc. as needed. Gentoro presently supports API keys and will soon add OAuth.

Gentoro generates and tests the code needed to connect to services automatically. If the task exceeds the LLM’s capabilities, Gentoro provides a workbench where users can offer hints to guide the LLM in refining the code. For example, a user might say, “The name is incorrectly being blanked,” which is often enough to correct the behavior without reviewing the code. In rare cases, a developer may need to review the code or provide more precise hints.

Gentoro can run in private data centers or in public clouds behind your firewall so data can remain within your security perimeter. Gentoro does not extract and store any data in its own environment and never takes possession of your data.

For enterprise use, data confidentiality is crucial, and sensitive data often cannot be shared with public LLMs or even within the organization. Gentoro ensures compliance with company policies through a rule-based privacy and security layer. Sensitive data, like Social Security numbers or HIPAA-related information, is automatically detected, tokenized, and securely handled. Data can also be encrypted and decrypted as needed, offering flexible and secure processing.

Yes, Gentoro supports LLM vendors that use lambdas, such as Amazon Bedrock. If the lambda option is chosen, Gentoro will generate and deploy the lambdas directly into the platform. Once deployed, prompts can be sent directly to the platform for execution without needing to go through Gentoro.

The technical expertise required depends on the complexity of the use case. For simple tasks, no coding is needed—Gentoro provides built-in tools that work right out of the box, or you can modify it by describing the missing features, all without writing code.

For more complex cases involving multiple APIs, Gentoro handles these scenarios, and coding is often unnecessary. A clear description of the issue usually resolves it. If needed, Gentoro provides developer tools for more advanced situations, but its primary goal is to empower non-coders while offering full support for developers when required.

Retrieval-augmented generation (RAG) is a technique that enhances prompts by supplementing them with facts from a knowledge base. Its purpose is to address the limitation of large language models (LLMs) that rely solely on their training data and may lack up-to-date or domain-specific information.

Gentoro, on the other hand, focuses on a different limitation of LLMs: their inability to control external systems. Gentoro utilizes a specialized LLM capability called function-calling, which allows it to execute API calls on behalf of the LLM, enabling interaction with external systems and services.