Understanding context can improve experiences between AI and humans
Context is essential in every human interaction. But in today’s digital age, understanding and leveraging contextual nuances is critical to improving the quality of our interactions. As we hone these skills, we will increase our ability to revolutionize the future of AI applications. Context encompasses the complex web of circumstances, settings, and factors that determine how we perceive information. We gain insights from situational, personal and social elements and these give us the clues we need to understand and interpret messages. Linguistically, context transcends words and delves into the extralinguistic elements that create meaning and understanding. Without context, meaning can be lost or misunderstood, underscoring its crucial role.
The broad spectrum of context
Context as a concept exists on a broad spectrum that ranges from broad backgrounds to detailed, current details. In general, context provides fundamental understanding; however, in a specific setting it can be much more nuanced depending on the specifics of the given situation. For example, when you respond to a request while cooking to also prepare a vegetable side dish, the context is not only the preparation of the food, but also the consideration of the likes and dislikes of the person making the request. This level of specificity enriches the interactions, allowing responses to be tailored to individual requirements and circumstances.
When considering AI and context, and in particular large language models (LLMs), this specificity is achieved using techniques such as retrieval-augmented generation (RAG), which integrates detailed contextual cues into questions and produces highly relevant and personalized answers. RAG shows how a nuanced understanding of context can improve the quality of the interaction between humans and AI.
How RAG improves personalization and context
Context is the most important factor in enabling personalization, especially in today’s ubiquitous digital environments. The process by which AI assesses a user’s past behavior, current circumstances, and preferences enables systems to deliver detailed, highly customized experiences. With AI, the concept of personalization goes beyond human interactions and embraces the Internet of Things (IoT), expanding context to include variables such as location, environment and historical data.
The role of RAG is to augment this process by finding and applying specific contextual data from a vast repository of vectors or embeddings. The vectors reflect different facets of a user’s profile or situational data, and they are essential for creating highly relevant responses that are also highly personalized. As vectors are collected, they build a library of historical patterns and current status, and this helps increase the AI’s understanding, allowing it to provide more specific and nuanced answers.
How inclusions work
Embeddings are mathematical representations, also called vectors, that play a crucial role in capturing and using context. They work by encoding various aspects of data, enabling nuanced profiling and semantic searches. The interaction between embedding and LLMs is cooperative, with embedding providing a dense contextual setting that helps LLMs with semantic understanding. The outcome is inevitably more accurate and contextually relevant.
As contextual vectors accumulate – a process called accretion – a more comprehensive understanding begins to develop, encompassing different types of interactions, customers, users, or situations. The context constructed to enhance the predictive and responsive capabilities of the AI system depends on the accuracy of the vector search. That’s why it’s so important to have high-quality, up-to-date data to inform these model responses.
Inferring improved responses by integrating context into LLMs
Providing contextual cues to LLMs will result in more polished and accurate in-context responses, which are essential for improving user interactions and decision-making. However, the application of context extends beyond the LLM framework, with additional layers of specificity that minimize response variance and ensure even greater relevance and personalization.
So let’s take a look at the capabilities needed to implement a system this context-aware. They start by having a huge high throughput vector store. Also needed is an efficient inclusion of embedding that ensures that the current context is preserved. The system will also need to have the ability to generate embeddings from various data sources and have access to models suitable for creating and applying embeddings. Finally, the most suitable base model for the task to be performed must be selected.
Looking ahead to the next phase of GenAI
In the age of generative AI, context is the cornerstone of valuable and significant interactions and effective decision-making. By understanding and applying the nuances of specific in-the-moment contexts, AI systems can deliver unparalleled personalization experiences and relevance, helping online service providers such as retailers, banks, search engines and streaming companies. There is a synergy between LLMs, RAG and embedding that is delivering a new paradigm in AI research and application, promising a landscape where interactions with AI are as nuanced and understandable as the ones we currently enjoy among ourselves.
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