In the world of artificial intelligence (AI), many people are excited by the headlines surrounding breakthroughs in natural language processing (NLP), conversational AI, and creative text generation. Yet, a critical element enabling these advancements often goes unnoticed: the context window.
While large language models like GPT-3 or GPT-4 steal the limelight, understanding developments in context window capabilities can help us understand how best to make use of these new models.
What is a Context Window?
At its core, a context window refers to the amount of information an AI model can process at once. When interacting with AI—whether through a chatbot, generating code, or asking questions—there's a limit to how much text or data the model can "see" at one time. This "window" acts as the scope within which the AI considers relevant context to generate meaningful and coherent responses.
For early AI models, this window was incredibly small, restricting the machine's ability to handle complex, long-form tasks or understand nuanced queries spread across multiple inputs. The context window determines how well an AI can grasp continuity, hold meaningful conversations, or generate long, detailed outputs. An AI with a larger context window can handle more intricate data, sustain more natural dialogues, and parse large amounts of structured or unstructured text efficiently.
The power of larger context windows
The context window isn't just about being able to handle long prompts. It has many implications for the ways we can interact with LLMs:
- Larger text comprehension: Longer windows enabled models to read and respond to more complex or lengthy inputs.
- Improved memory over conversations: Models could maintain better continuity and context over multiple exchanges in conversations, improving user experience.
- Handling of structured data: Models became more adept at tasks such as coding, where they needed to process blocks of information that span across different functions or files.
The science behind context windows
Early Models: Limited Context
AI as we know it today didn’t come about suddenly. It’s an evolution of machine learning and neural network science that has captured headlines throughout the 2010s. In the early days, models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks were foundational for language tasks. However, these models struggled to manage long sequences of text. They could only "remember" a few tokens (words or phrases) at a time, causing them to forget information quickly. The ability to capture context over long passages was limited, constraining the models to simpler, shorter tasks.
2017: Transformers and Self-attention: the age of Natural Language Processing
The landscape of AI changed dramatically in 2017 when researchers introduced the Transformer architecture in the paper “Attention is All You Need.” Transformers rely on a self-attention mechanism, a neural network technique that allows a model to weigh the importance of various parts of an input. It’s the basis for natural language processing. This key innovation enables models to capture dependencies between words in a sentence or across multiple sentences far more effectively.
2018-2021: Early GPT
The introduction of the Transformer paved the way for Generative Pretrained Transformers (GPT), with OpenAI’s GPT-2 (2019) and GPT-3 (2020) becoming well-known. The term “generative” refers to the model’s ability to generate text, and “pretrained” indicates that the model is initially trained on vast amounts of data before being fine-tuned for specific tasks.
The context windows for these models were initially 512 tokens, then expanded to 1024 for GPT-2, and 2048 for GPT-3. These increments opened up new possibilities for text generation, allowing for richer and more detailed outputs. For example, GPT-3’s 2048-token window allowed it to maintain coherent multi-turn conversations and generate essays, stories, or articles several paragraphs long. However, even these were limited when working with long documents or extended conversations that spanned many different themes or topics.
2023: GPT-4 and Ring Attention
In 2023, GPT-4 was released with multiple variants, one of which had a massive 32,768-token context window—equivalent to around 50 pages of text. This larger window allowed the AI to take on even more advanced tasks, such as analyzing long legal documents, summarizing entire books, or providing multi-step reasoning over lengthy conversations.
One of the key advancements for this has been ring attention, which allows an LLM to process all parts of an input sequence in parallel, rather than sequentially. In doing so, it helps to overcome the massive computational requirements that a long context window generally demands.
This advancement opened up exciting possibilities for using AI in industries like law, academia, and journalism, where the ability to process large documents efficiently is critical. Instead of breaking down content into smaller, more manageable parts, users can provide entire datasets or texts at once, and the model can provide in-depth, detailed outputs.
Downsides of long context windows and how to mitigate them
Increased Computational Costs: Larger context windows require more computational resources, making models more expensive to run. In our AI programs, we mitigate this through good old testing and iteration. Vetting outputs on a smaller scale, rolling out on a limited basis, and then scaling as the use case gains trust helps us limit how much we spend on lower-value outputs. Read more about how we're operationalizing AI here.
Difficulty in Maintaining Coherence: With larger windows, it can become challenging for models to keep the conversation coherent, as it might struggle to weigh which information is most relevant. More information can mean more opportunities for the model to become confused. You can mitigate this by having cleanly structured prompts with clearly numbered steps and tasks. Read more about effective prompt structuring here.
Input quality matters: Garbage in, garbage out is a perpetual adage in the data world, but it's as true here as in your data warehouse. With large context windows and large amounts of information in the prompt comes the potential for conflicting information that leaves the model confused, unexpected data structures the model can't interpret, and other challenges. To make use of these context windows, having tested prompts that fit a clean, working template structure is paramount.
How to make use of longer context windows
Longer context windows have practical applications that are reshaping how AI is used.
Deeper conversations with agents: More context means more understanding. Agents built on models with long context windows can understand more of the conversation, but can also take in more information about the person they're interacting with, such as order histories or loyalty program status.
Guardrail-informed generative outputs: Large context windows allow a generative model to take in more information to guide its output. For instance,
In the data space specifically, unlocking the ability to process large amounts on information can power incredible data enrichment and discovery use cases. Here are our favorite ways we've made use of long context windows in-house:
Retrieval-augmented generation (RAG): Is a system where models don’t just rely on a fixed window of context but can “retrieve” relevant information from external databases or documents when generating a response. This hybrid approach greatly expands the model’s effective context by incorporating dynamic, real-time knowledge retrieval, enabling it to answer complex questions or create highly informed content. Research from Databricks has shown that the longer the context window, the better the performance of RAG systems.
Structured Data Ingest: Another exciting use case involves passing structured data formats, such as JSON files, into models. JSON files often contain hierarchical or nested information, requiring the AI to process large sets of data with intricate relationships. Models with longer context windows can now handle these complex data formats, enabling them to analyze datasets, generate summaries, or even write code based on the input. See how we use JSON inputs in our AI strategy here.
Conversational memory: Longer context windows have upped the standard for interactions with AI agents
The future of context windows
As research into context windows continues, future models may handle hundreds of thousands or even millions of tokens, unlocking capabilities that currently seem unattainable. Such models could analyze entire research papers, generate detailed legal analyses from case histories, or even create novel-length stories with deep character development, thematic consistency, and intricate plots.
Dynamic and adaptive context windows are also being explored. These allow the model to adapt its attention dynamically based on the relevance of earlier context, which could bring improvements in efficiency and performance.
Though often overlooked, the context window is one of the most transformative advancements in AI’s history. By allowing models to handle larger and more complex sequences of information, the expanded context window has revolutionized how we use AI for natural language processing, coding, and more. As research continues, it promises to unlock even greater possibilities for what AI can achieve, from deeply understanding long documents to generating even more sophisticated outputs across various fields.