Generative AI
The first step in developing applications based on generative AI is to select the appropriate large language model (LLM). There are several open source options to choose from—including Bidirectional Encoder Representations from Transformers (BERT), Text-to-Text Transfer Transformer (T5), and Granite models—each offering unique strengths for different tasks. It’s important to select an LLM that aligns to your application's objectives. For instance, Granite-7B-Starter can be fine-tuned for summarizing insurance-specific text that highlights risk factors, coverage, and liabilities, while BERT excels in sentiment analysis.
Evaluating model performance is crucial, as LLMs vary in accuracy, fluency, and overall efficacy for tasks relevant to your applications. Additionally, high-capability models like GPT-3 and some Granite variants can require significant computational resources, including expensive graphics processing unit (GPU) resources, so it's essential to balance these needs against your available infrastructure and budget. And with access to sufficient high-quality data for fine tuning, you can ensure optimal LLM performance that meets application requirements.
Frameworks like Langchain simplify the integration of LLMs into applications, allowing you to focus on the core application logic. These frameworks offer tools for prompt engineering and model chaining, while enhancing LLM-based components with memory or context.
After selecting the optimal LLM and frameworks, you are ready to add generative capabilities into your applications. This process involves refining the model's performance and crafting precise and effective prompts that guide the AI to deliver desired outcomes. Establishing robust feedback loops is crucial for continuous improvement, ensuring the model adapts and enhances its outputs over time.
Prompts help you instruct the LLM to generate the desired output. By creating clear, concise prompts, using templates for structured instructions, and employing techniques like chaining to guide the LLM through complex tasks, you can significantly enhance the model's effectiveness. These strategies ensure that AI models produce consistent and relevant responses, even in multistep interactions.
The reinforcement learning from human feedback (RLHF) loop is crucial for fine tuning your LLM. After deploying your model, gather user interactions and use this feedback to refine the LLM performance. This iterative process helps your model learn from mistakes and continuously improve, increasing its ability to deliver accurate and relevant outputs as it adapts to real use cases.
Fine tuning further customizes pretrained LLMs to fit your specific domain or task. By training models on smaller, task-specific datasets, you can enhance performance and customize outputs to meet your application requirements. Tools like Hugging Face Transformers let you take advantage of the pretrained model's knowledge while refining it for your purposes. The model alignment method from InstructLab helps you align the model's outputs with your organizational values or user needs, ensuring responses are accurate and contextually appropriate.
Retrieval-augmented generation (RAG) combines LLMs with information retrieval systems, allowing models to access and incorporate relevant data from external sources during generation. This approach improves the factual accuracy and coherence of the outputs and is often used when augmenting LLM results with internal and corporate data. Langchain's built-in RAG capabilities streamline this process, especially when using Granite models to produce accurate and contextually relevant responses.
Agents are autonomous systems that operate within a defined environment to achieve specific goals. By incorporating interactive and adaptive behaviors, these systems can dynamically modify their operating context to respond to changing conditions. This allows them to handle complex tasks and make real-time decisions. Developing these agents involves constructing multicomponent systems that plan, execute, and evaluate actions based on AI model outputs. By orchestrating complex tasks—including real-time decision making and external API and data source integration—you can enhance your system's operational capabilities.
Model chaining connects multiple AI models or processes into a cohesive workflow, where each model builds on the outputs of the previous one. This approach allows you to develop applications capable of handling complex tasks with multistep interactions. By using the capabilities of different models in a coordinated sequence, you can build efficient systems tailored to your requirements.
By thoroughly evaluating your application’s workflow with the integrated AI, you can ensure a user-friendly and efficient experience. Rigorous testing of the entire system helps you identify and address any issues or inefficiencies, allowing you to refine the application for improved functionality and usability. This iterative process not only enhances performance but also aligns the application more closely with user needs and expectations.
