Structural Advancements Developed for Language Models to Support Application Creation

The evolution of Language Models has driven the need for supporting frameworks and products to transform raw capabilities into scalable, enterprise-ready solutions.

These frameworks address specific vulnerabilities in LMs, such as factual inaccuracies, hallucinations and unstructured outputs, enabling their effective deployment in real-world applications.

Key challenges arise from three areas…

𝗙𝗶𝗿𝘀𝘁, organisations must identify how to harness LMs’ strengths while mitigating their weaknesses.

𝗦𝗲𝗰𝗼𝗻𝗱, frameworks need flexibility to integrate new features from model updates.

𝗧𝗵𝗶𝗿𝗱, these solutions risk obsolescence if models incorporate their functions directly — much like how early flashlight apps became irrelevant once smartphones offered built-in flashlights.

Hence staying ahead requires foresight into emerging model capabilities and accommodating future advancements. A case in point is frameworks which can leverage model vision for computer use.

Simultaneously, commercial model providers aim to counteract agnostic frameworks by developing proprietary tools and services, creating ecosystems to maintain user dependency.

A dynamic which illustrates the interplay of innovation and competition within the AI ecosystem.

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.