Augmentation
The process of enhancing AI model capabilities by connecting them to external data sources, tools, or knowledge bases. RAG (Retrieval Augmented Generation) is a prime example.
In-Depth Explanation
Augmentation addresses a fundamental limitation of language models: their knowledge is frozen at training time and they can hallucinate information. By augmenting models with external resources, we can provide them with current, accurate, and domain-specific information.
The most common form is Retrieval Augmented Generation (RAG), where the model retrieves relevant information from a knowledge base before generating a response. This grounds responses in factual data rather than relying solely on trained knowledge.
Beyond RAG, augmentation includes:
- Tool augmentation: Connecting models to calculators, search engines, databases, and APIs
- Memory augmentation: Giving models access to conversation history and learned preferences
- Knowledge augmentation: Linking to structured knowledge graphs and ontologies
- Multimodal augmentation: Adding capabilities to process images, audio, or other data types
The key benefit is that augmentation allows businesses to make AI accurate about their specific context without the expense of fine-tuning or training custom models.
Business Context
Augmentation lets you make AI models more accurate and relevant to your business by grounding them in your specific data and context, without expensive retraining.
How Clever Ops Uses This
Augmentation is central to our implementation approach at Clever Ops. We specialise in RAG systems that connect AI models to your business data, ensuring accurate responses about your products, policies, and processes.
Example Use Case
"Augmenting a chatbot with your product documentation so it can answer specific questions accurately without hallucinating features."