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Gemini fine tuning is here

In The Intelligent Tsunami, I describe how deep neural nets can be fine-tuned for specific applications. Google announced today that:

Tuning can improve how Gemini models perform for specific tasks by training the model with new data. Using a tuned model may improve latency by reducing the amount of context needed in prompts and helps responses adhere to specific output requirements. Customize Gemini 1.0 Pro in minutes with Google AI Studio or by using the Gemini API.

The book describes The API Approach to developing an intelligent agent.

A robust intelligent agent can be created using an application programming interface (API) to a deep neural network, like an LLM. An API is software that sends information back and forth between a website or app and a user. Several LLM platforms provide APIs, including OpenAI's GPT-3, LaMDA, and Jurassic-1 Jumbo, among others. 

The first few steps are similar whether the intelligent agent is being developed through a no-code or an API approach. 

  1. Define your purpose and audience by identifying the tasks your agent will perform, the target audience, and the tone and personality of the agent. 

  2. Select the right API, considering factors like capabilities, pricing, data privacy, and ease of use in selecting the right platform. 

  3. Design your agent's architecture, deciding how the agent will interact with users, such as text interface or voice assistant. Plan the conversation flow using prompts, directives, and conditional logic. Consider error handling and graceful fallback mechanisms.

From here, the steps to use an API are different from a no-code approach. 

  1. Develop your agent's code by choosing a programming language and framework, like Python or Node.js. Integrate the API into the intelligent agent application using the language’s documentation instructions. Implement the designed conversation flow and user interaction logic. Remember security best practices when handling user data and API keys.

  2. Train and refine your agent using an API’s training tools or custom scripts, which allow for more robust training of the DNN. Provide the API with relevant training data, such as text prompts and responses. Continuously test and iterate based on user feedback and performance metrics.

  3. Deploy and integrate your agent on a hosting platform that suits your needs, such as a cloud service or local server. Integrate your agent with your user interface or application. Monitor performance and gather user feedback for further improvements.

Understand the API's data usage policies and ensure compliance with relevant regulations. Implement best practices to protect user data and API credentials. Be mindful of potential biases and unintended consequences of your agent's responses.

Building an intelligent agent with an API has several advantages over a no-code approach.

APIs generally offer more access to the underlying AI model and its training data, allowing for better fine-tuning and customization of the AI's capabilities and responses to better suit your specific needs. APIs don't rely on pre-built templates or limited functionalities. You can build bespoke features and tailor the agent's behavior to your exact requirements. APIs integrate with your existing infrastructure and data sources, enabling access to your internal data for more personalized and informed responses.

For complex tasks requiring specific domain knowledge or nuanced understanding, APIs offer more flexibility to build sophisticated conversation flows and logic. APIs can connect agents to powerful language models like GPT-4, potentially unlocking advanced capabilities like creative text generation, complex information retrieval, and deep reasoning. APIs often offer better scalability to handle larger volumes of users and integrate with additional systems.

An API approach also has drawbacks. Using APIs demands more technical knowledge and development skills than a no-code approach. A developer is needed for setup, customization, and maintenance. Building and maintaining an API-based agent can be more time-consuming and expensive compared to the quicker, and often cheaper, no-code option. Integrating with external APIs introduces additional security considerations that need to be addressed carefully.

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