Augmented LLM

An Augmented LLM is the fundamental building block of agentic systems. It consists of a Large Language Model (LLM) enhanced with additional capabilities that extend its basic functionality. These enhancements, sometimes referred to as augmentations, enable the model to interact with external tools, maintain context, and make informed decisions.

Current LLMs can actively use these capabilities, generating their own search queries, selecting appropriate tools, and determining what information to retain.

Key Capabilities

Instead of listing general capabilities, let's provide specific examples:

• External tool integration: This could include accessing APIs like Google Maps for travel information or using a search tool to retrieve relevant information.
• Information retrieval: Augmented LLMs can access and retrieve relevant information from knowledge bases or databases.
• Memory management: The LLM can store and recall past interactions and information, enabling it to maintain context and continuity in conversations.
• Search query generation: The model can formulate search queries to find specific information from external sources.
• Context awareness: The LLM can understand and respond to user queries based on the current conversation and past interactions.

Common Augmentations

1. Tool access and control: This allows the LLM to interact with external tools and services, expanding its capabilities beyond text generation.
2. Knowledge retrieval systems: Augmented LLMs can be connected to knowledge bases, databases, or search engines to retrieve and utilize external information.
3. Memory mechanisms: The LLM can be equipped with memory mechanisms to store and retrieve information from past interactions, enabling it to maintain context and recall relevant details.
4. Environmental feedback loops: Augmented LLMs can receive feedback from their environment based on their actions, allowing them to learn and adapt their behavior.
5. Decision-making frameworks: The LLM can be integrated with decision-making frameworks to guide its actions and choices based on pre-defined rules or learned patterns.

Implementation Considerations

• Careful tool documentation: Clear and concise documentation of the tools available to the augmented LLM is crucial for its effective utilization. This includes specifying the exact structure and definition of tools in the API.
• Clear interface definitions: Well-defined interfaces between the LLM and its augmentations ensure seamless communication and data exchange.
• Proper error handling: Mechanisms for handling errors and unexpected situations are essential for building robust and reliable augmented LLMs.
• Performance optimization: Optimizing the performance of the augmented LLM, including minimizing latency and computational costs, is important for real-world applications.
• Security considerations: Security measures should be implemented to prevent unauthorized access to the LLM, its tools, and sensitive data. This could include sandboxing the environment.