Exploring New Frontiers in AI with the Real-Time Query Model

May 14, 2023

In the world of artificial intelligence, Large Language Models (LLMs) like GPT-4 have emerged as powerful tools capable of understanding and generating human-like text. These models have shown remarkable capabilities in a variety of areas, from drafting emails and writing code, to creating poetry and even composing music. They have also demonstrated an ability to think creatively and solve complex problems to a certain extent.

However, despite these impressive feats, LLMs are not without their limitations. One significant issue is their inability to access real-time data. Consequently, their understanding of the world is effectively frozen at the point of their last training update, preventing them from providing information that has emerged since then.


In addition, while LLMs have displayed some problem-solving and creative capabilities, a substantial portion of their training and computational resources is dedicated to memorizing an array of facts. In essence, LLMs function largely as massive, intricately structured fact databases. While this allows them to regurgitate a wide array of facts and figures, it also means that much of their potential for reasoning and creativity is untapped.

In this blog post, I propose an innovative approach that seeks to shift the focus of LLMs from fact storage to enhanced reasoning. By enabling LLMs to access and integrate real-time data from their training corpus during inference, I aim to transform these models into dynamic reasoning engines. This new paradigm could open up exciting possibilities, enabling LLMs to provide more accurate, current, and contextually relevant responses, while making better use of their training and computational resources.

We'll delve into the details of this approach, discuss its potential applications, and explore the challenges that lie ahead. I invite you to join us on this journey of exploration and discovery in the fascinating world of AI research.

Current State of LLMs and the Need for Change

Large Language Models, in their current state, are marvelous feats of modern AI research. Their ability to generate human-like text and perform tasks requiring linguistic comprehension is a testament to the sophistication of these models. While they demonstrate an ability to think creatively and solve complex problems, it's crucial to understand that a significant portion of their training resources and computational power is spent learning to memorize facts.

The process of training these models involves exposing them to vast amounts of text data, from which they learn the statistical patterns that govern human language. They also absorb a wealth of factual information contained within these texts. Consequently, a large portion of the model's 'knowledge' is essentially a static snapshot of the world as of the time of training. This means that the model's understanding is frozen at the point of the last update and can't incorporate any information or events that have occurred since.

This approach presents two fundamental issues. First, it means that the model is unable to provide up-to-date information. For example, if an LLM was trained up until 2021, it would not be able to provide accurate information about events that occurred in 2023. Second, the model's focus on memorizing facts detracts from its ability to develop as a reasoning engine. While memorizing facts is not inherently bad, the cost of doing so — in terms of training time, computational resources, and energy — is immense. This focus on fact storage often comes at the expense of the model's potential for reasoning and creativity.

The vision for Large Language Models is not just to act as a repository of past knowledge, but to serve as dynamic reasoning engines that can understand, learn, and provide insights based on the most current and contextually relevant information. However, achieving this vision requires a shift in how we approach the training and operation of these models. Instead of focusing on fact storage, we need to refocus on enhancing the models' reasoning capabilities and their ability to access and integrate real-time data.

In the following sections, I will explore a new approach that aims to address these issues, shifting the paradigm of LLMs from static fact databases to dynamic reasoning engines.

Proposed Solution: A New Paradigm for LLMs

In order to overcome the limitations of the current state of Large Language Models, I propose an innovative approach that aims to transform these models from static fact databases into dynamic reasoning engines. The core idea is to shift the focus of LLMs from fact memorization to reasoning, and to allow for real-time data access during inference.

This approach involves introducing a new concept into the training and operation of LLMs: the ability to dynamically access and integrate data from their training corpus during inference. This capability would allow LLMs to provide more accurate, current, and contextually relevant responses, while making better use of their training and computational resources.

Here's how it would work in practice:

  1. Reserve a portion of the context window for real-time data retrieval: The context window of an LLM, which is the amount of recent input information the model considers when generating a response, would be partitioned to include a reserved section for real-time data access. This section would serve as a 'live' data feed, providing the model with up-to-date information from its training corpus.
  2. Implement data retrieval queries: The model would be trained to generate specific queries in the form of tokens, which would prompt the retrieval of relevant data from the training corpus. This data would then be appended to the context window, serving as part of the input for the model's next response.
  3. Train the model to manage its 'live' data feed: The model would also be trained to manage this 'live' data feed, learning when to update it and how to integrate the retrieved data into its reasoning processes.

By introducing the capability for real-time data access and integration, we could fundamentally change the role of LLMs. In the conventional model, an LLM must 'remember' a vast amount of factual information encountered during its training to answer a wide range of possible queries. This necessitates substantial computational resources and time for the model to learn and store these facts, often at the expense of its reasoning capabilities.

However, with my proposed solution, the LLM no longer needs to remember all these facts. Instead, it can retrieve relevant, up-to-date information as needed from the training corpus. This shift allows the model to dedicate more of its training resources towards enhancing its reasoning abilities, such as understanding complex contexts, making logical deductions, and generating creative solutions.

In addition, this approach also addresses the issue of the LLM being locked to the state of the world at the time of training. By integrating real-time data access, the model can dynamically adapt to new information, providing insights that are current and relevant.

In essence, this new paradigm transforms LLMs from static fact databases into dynamic reasoning engines. The shift in focus from fact storage to reasoning and real-time data access could potentially lead to more efficient use of training resources and pave the way for more capable and versatile LLMs.

In the next section, I will delve deeper into the technical details of this proposed solution.

Technical Implementation of the Proposed Solution

Implementing this new paradigm for Large Language Models (LLMs) involves several key steps and considerations:

  1. Reserving Context Window for Real-Time Data: A portion of the LLM's context window, which determines the amount of recent information the model uses to generate a response, must be reserved for real-time data. This partitioning is performed during the training phase, allowing the model to learn how to utilize this space effectively.
  2. Special Tokens for Queries and Data Results: We would need to introduce special tokens that the model can output to signal a query and the incorporation of data results. These could be, for instance, [START_QUERY] and [END_QUERY] for the start and end of a query, and [START_RESULT] and [END_RESULT] for the start and end of the data results.
  3. Generating Data Retrieval Queries: The model would be trained to generate effective queries between the [START_QUERY] and [END_QUERY] tokens. This could be achieved using a supervised learning approach, where examples of correct queries for given prompts are provided.
  4. Data Retrieval: An external system would interpret and execute the queries generated by the model. This system would search the training corpus using cosine similarity with the embeddings of the query tokens, and return the most relevant data.
  5. Incorporation of Data Results: The retrieved data would be appended to the context window, surrounded by the [START_RESULT] and [END_RESULT] tokens. This lets the model distinguish the data results from the original context, treating it as a part of its 'live' data feed.
  6. Managing the 'Live' Data Feed: The model would also be trained to manage this 'live' data feed effectively. This includes determining when to request updates, how to integrate the retrieved data into its reasoning process, and possibly even deciding when to discard outdated or irrelevant information from the context window.
  7. Training: Adjustments would need to be made to the model's training process to teach it to use these new mechanisms effectively. This might involve a combination of supervised learning and reinforcement learning techniques, with the aim of balancing the trade-off between query effectiveness and coherence of responses.

This technical implementation introduces several challenges. Data retrieval and integration need to be efficient to ensure the model's responses are timely. The increased model complexity may require more advanced hardware or innovative optimization strategies. Maintaining coherence in the model's responses, given the changing context window, poses a significant challenge.

Despite these challenges, surmounting them could yield a new generation of LLMs that are more capable, more versatile, and better aligned with my vision of AI as a dynamic reasoning engine.

Challenges and Future Work

The implementation of real-time data integration in Large Language Models is a compelling idea, but it is not without its share of challenges. In this section, I will delve deeper into these potential obstacles and propose directions for future research.

  1. Efficient Data Retrieval and Integration: The process of retrieving and integrating real-time data must be efficient to ensure the model's responses are generated in a timely manner. Future work could explore the use of advanced search algorithms, hardware acceleration, or machine learning techniques to optimize this process.
  2. Model Complexity and Hardware Requirements: The proposed solution may increase the model's complexity, potentially requiring more advanced hardware or innovative optimization strategies. Future research could investigate techniques for managing this increased complexity, such as novel model architectures, training methods, or hardware solutions.
  3. Coherence of Responses: Maintaining coherence in the model's responses, given the dynamically changing context window, poses a significant challenge. Future work might focus on developing strategies for ensuring the model can effectively integrate new data into its reasoning process without disrupting the flow of its outputs.
  4. Training the Model to Formulate Effective Queries: The model must learn to formulate effective queries based on the context and its current conversational goal. Future research could explore supervised or reinforcement learning approaches for this, or possibly investigate ways of enabling the model to learn from its past query successes and failures.
  5. Parallelization and GPU Capabilities: Given the real-time nature of the proposed solution, it will be crucial to leverage parallel computation and GPU capabilities for efficient data retrieval and integration. Future work should consider how to design the system to maximize these capabilities.
  6. Ethics and Misuse Potential: As with any advancement in AI, potential misuse and ethical implications must be considered. For instance, an AI that can query a vast corpus of data in real-time could potentially be used to spread misinformation or manipulate conversations. Future research should investigate safeguards to prevent misuse.

Addressing these challenges will require a concerted effort from the AI research community. However, the potential benefits — creating a new generation of LLMs that are more capable, more versatile, and better aligned with my vision of AI as a dynamic reasoning engine — make it a pursuit worth undertaking.


The field of AI has come a long way since its inception. With the advent of Large Language Models (LLMs), we now have machines that can generate human-like text, answer questions, and even perform some degree of reasoning. Yet, these models are far from perfect. They suffer from a lack of real-time data access, they often function as static fact databases rather than dynamic reasoning engines, and their context windows limit the range of information they can consider while generating responses.

This blog post has proposed a novel solution to these problems: enabling LLMs to query a large data corpus in real time and incorporate the retrieved data into their context window. This solution has the potential to transform LLMs into more effective reasoning engines that can pull in relevant, real-time data as needed, rather than relying on static information learned during training. It could also allow us to specialize models post-training by changing the accessible data, without the need for additional training.

While the technical implementation of this solution presents several challenges, including efficient data retrieval and integration, increased model complexity, maintaining coherence of responses, and ethical considerations, I believe these challenges are surmountable. Indeed, they present exciting avenues for future research.

AI is, by its very nature, a field that pushes boundaries. It's about imagining what might be possible, and then finding ways to make those possibilities a reality. This proposal represents just one idea in a sea of potential innovations that could shape the future of AI and its impact on our world. I look forward to seeing how this concept evolves and what the AI research community can achieve in the years to come.

Invitation to Engage

Before we conclude, I'll share a bit about myself. I am a software engineer and founder, with experiences at places like Bridgewater Associates and NVIDIA. These roles have deeply influenced my perspective on AI, instilling in me a keen understanding of the challenges we face and the potential solutions within our grasp.

Now, I invite each one of you to take part in this important conversation. Whether you're an AI researcher, a data scientist, a fellow software engineer, or just an enthusiastic observer, your insights and ideas are critical. Reflect on the limitations of Large Language Models we discussed. The lack of real-time data access, the immense resources dedicated to fact memorization, and the potential for greater reasoning capabilities - these are the issues at hand.

I encourage you to share your thoughts and feedback on these topics. How do you perceive these problems? What impact do they have on AI's potential? What alternative solutions can we explore?

Those of you in the field, I urge you to probe these issues in your work. Your research and experimentation could pave the way for groundbreaking advancements, bringing us closer to unlocking the full potential of AI.

Let's propel this conversation forward, spreading awareness and sparking innovation. Please share this post within your networks and let's ignite a global discussion. The journey towards a more capable, more versatile AI begins with a single step - this is ours.

I'm eager to hear your thoughts and excited to see where this journey takes us. Let's explore these challenges together, and shape the future of AI!