The Evolution of Language Models: From Knowledge-Intensive NLP to Multi-Modal Reasoning
The Progression from Static Knowledge to Dynamic Contextual Understanding & Reasoning
In Short
Over the past few years, language models have shifted away from a focus on knowledge intensity — memorising vast arrays of incidental facts scraped from the internet — toward prioritising model behaviour.
This behaviour emphasises breaking tasks into subtasks, reasoning through them, and delivering coherent outcomes rather than merely predicting the next token.
Innovation from Language Model providers has also been less on the API frameworks and more focussed on end-user UI development to enhancing the end-user experience.
Again, these include elements like LMs operating browsers and computer GUIs, context management, options like research, synthesising disparate data sources and more.
Models now stream their reasoning in real-time, offering users a window into their internal logic — an advance that introduces greater observability and inspectability.
The Progression of Language Models
Language Models have undergone significant transformation, evolving from static knowledge repositories to dynamic, multi-functional AI systems.
In this article, I explore the progression of language models, their capabilities and the frameworks that is evolving to support the ever-expanding functionalities.
Data and Content Progression
Initially, language models were designed to be knowledge-intensive. Meta introduced the term “Knowledge-Intensive NLP” (KI-NLP) to describe models that relied heavily on vast datasets for factual accuracy.
However, it soon became clear that while these models could store and retrieve vast amounts of information, they were not always reliable in production environments, particularly within enterprise AI frameworks.
One of the biggest challenges was hallucination, where models generated highly plausible yet factually incorrect responses. To address this, AI research shifted towards in-context learning, where models prioritise contextual reference data provided during inference rather than solely relying on their pre-trained knowledge.
This approach needed to scale beyond individual prompts to a broader framework-level implementation, enabling retrieval-augmented generation (RAG) systems to efficiently integrate external data sources.
By 2024, a surge of products emerged to enhance RAG frameworks, allowing large language models to incorporate relevant data dynamically and ground their responses more effectively.
This shift led to the introduction of grounding principles, where users could upload documents directly into a no-code user interface.
These documents then acted as real-time contextual references, ensuring more accurate and relevant AI-generated responses.
The Role of Reasoning in AI
Introduction of Agentic Features In Language Models
As I’ve previously written, the concept of Chain-of-Thought (CoT) reasoning, introduced by a Google research paper, has become a fundamental principle in AI advancements. CoT reasoning can be implemented in three key ways:
- Prompt Engineering: Language Models are explicitly instructed to follow a step-by-step reasoning process to improve accuracy. By breaking down complex problems into sequential sub-tasks, the accuracy of the Language Model output is greatly enhanced.
- No-Code Prompt Chaining: This approach involved connecting/chaining multiple prompts together, where the output of one prompt served as the input for the next. While effective, it proved difficult to scale efficiently. This approach is also very rigid and follows a pre-determined flow of events.
- Agentic Frameworks: These frameworks make primarily use of a ReAct approach to reason and then act.
Models started internalising reasoning, and a number of reasoning models were released. Where the model performs internal decomposition of a problem, and follows a reasoning methodology in an automated fashion.
A fascinating development in this space is real-time reasoning transparency.
Language Models, such as DeepSeek Chat and Xai Grok3, have started streaming their internal reasoning steps, allowing users to observe how conclusions are reached.
OpenAI introduced Language Model reasoning, but users were charged for the underlying token usage without visibility into the actual reasoning process.
Now, models are shifting towards making their internal logic more accessible and transparent to users.
Another significant advancement is the integration of external tools and functions, allowing AI models to connect with external APIs, graphical user interfaces, and other external systems.
This expands their usability beyond simple conversational interfaces, enabling direct interactions with the outside world.
Language Models Adopting Agentic Characteristics
What’s particularly intriguing is that AI models themselves are evolving into autonomous AI Agents, adopting features previously reserved for agent-based frameworks.
While agent frameworks remain essential for complex enterprise applications, AI models are increasingly incorporating agent-like functionalities natively.
This raises the question: What does this mean for builder tools?
While AI models may serve individual users effectively through managed environments like ChatGPT, organisations will still require granular control, customisation, and advanced development frameworks to maintain enterprise-scale AI governance.
UX & General Functionality
Modern language models have become multi-modal in more ways than one. Standard features within AI-powered user interfaces now include:
✅ Document Uploads: Users can upload reference materials to improve contextual accuracy.
✅ Code Generation & Execution: the LM UI can not only generate but also execute and debug code.
✅ Web-Based Research: Language Models can search the web & synthesise information from multiple sources.
✅ Enhanced User Context Awareness: Models can adapt based on user-provided bios, preferences, and past interactions.
The boundary between language models as such & user interface functionality is rapidly blurring.
Language Model providers are integrating features directly into their models rather than relying on external software layers.
Looking ahead, the shift towards code-based interfaces and tiered AI access models is likely to accelerate.
As AI companies prioritise mass-market adoption, developers may find themselves deprioritised in favour of more consumer-friendly applications.
However, the demand for developer-centric AI tools remains strong, ensuring that specialised AI-driven software development will continue to thrive.
Final Thoughts
The evolution of language models is shifting towards real-time contextual grounding, multi-modal reasoning & problem solving and integrated tooling. While consumer Language Model interfaces are becoming more seamless, enterprises will still require customisable, scalable frameworks to deploy Language Models effectively.
As models continues to evolve, the challenge will be balancing ease of use for general consumers with powerful capabilities for developers and enterprises.
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
