This is a guest post for the Computer Weekly Developer Network (CWDN) written byChris Mahlin his role as CEO at Pryon.
Pryonis an enterprise knowledge management platform designed to simplify and accelerate the adoption of artificial intelligence.
Mahl writes in full as follows…
The AI industry’s obsession with massive models reminds me of the early days of computing – when everyone thought bigger meant better. But my experience leading Pryon has shown me something different: precision and thoughtful architecture matter more than raw size.
Size doesn’t matter (pipeline design does)
Let me share what we’ve discovered about combining Retrieval-Augmented Generation (RAG) with Small Language Models (SLMs). The key isn’t the model size – it’s how you design the retrieval pipeline. Think about the last time you had to find a specific document in a massive corporate database. It’s not about having access to everything; it’s about finding exactly what you need.
That’s what our RAG pipelines do – they cut through the noise to deliver precise, relevant context to these smaller models.
Small talk, big results
The real breakthrough comes in how we approach model specialisation with SLMs. While prompt engineering provides a foundation, it’s only the beginning of our optimisation journey. We’ve developed a comprehensive approach that spans multiple techniques:
Prompt Optimisation: Creating clear, specific instructions for handling information gaps and response formatting establishes baseline performance.
In-Context Learning: By providing SLMs with relevant examples within the prompt, they can adapt to specific tasks without parameter updates, significantly improving contextual understanding.
Task-Specific Fine Tuning: When we need deeper specialisation, we finetune models on carefully curated datasets representing specific enterprise use cases, creating purpose-built models that outperform larger generalist counterparts.
End-to-end Integration: The combination of models that makeup our offerings are collectively optimised to create a powerful synergy that maximises end-to-end performance, enabling the customers we serve to get the answers they need from vast knowledge bases, by simply asking their questions.
This has proven remarkably effective in enterprise environments, where specific, accurate responses matter more than general capabilities.
By investing in the full spectrum of specialisation techniques rather than simply scaling model parameters, organisations can deliver solutions that are not just more efficient but also more effective at meeting real business needs.
Edge-y business
Edge deployment runs AI models directly on local devices or nearby servers instead of distant cloud datacentres. This processes data where it’s created, eliminating transmission delays and connectivity dependencies.
NGO workers in remote locations can instantly access decades of research without internet connectivity. Financial analysts can run sensitive analyses locally, keeping confidential data secure. In real-world applications, one of our healthcare partners implemented SLMs at the edge with precomputed embeddings to access patient records and medical guidelines instantly, even in areas with poor connectivity, while maintaining strict privacy standards.
The resource efficiency of SLMs allows them to run on standard hardware while delivering specialised intelligence exactly where it’s needed. This transforms how organisations deploy AI, bringing powerful capabilities to environments previously considered impractical for advanced computing and democratising access across geographical and infrastructure barriers.
Pryon CEO Mahl: Each obstacle we overcome advances our understanding of what’s possible with smaller, smarter models.
The environmental impact can’t be ignored. Running massive AI models consumes enormous energy. Our approach with SLMs significantly reduces GPU requirements and energy consumption. This isn’t just about cost savings – it’s about responsible innovation. Every deployment decision we make considers both performance and environmental impact.
Challenges & opportunities
Of course, we face challenges. Balancing model size with accuracy requires careful optimisation. Data privacy demands robust security measures. Scaling successfully across diverse use cases requires robustness and rigorous validation and testing. But these challenges drive innovation.
Each obstacle we overcome advances our understanding of what’s possible with smaller, smarter models.
Looking ahead, I see several trends shaping our industry:
A resurgence in highly specialised, task-specific small models. Task-specific SLMs leveraging both general pretraining and task-specific customisation will be a foundation of progress.
Hybrid systems blending edge and cloud capabilities. Leveraging advantages of both paradigms to jointly optimise efficiency and performance.
Expanded multimodal processing. Specialized SLMs hardenedfor difficult multimodal tasks within optimised pipelines to overcome the “information soup” of our reality.
Fully Customizable Solutions – Specialised SLMs with flexible orchestration combining manual and automated agentic pipelines to deliver true value.
Those who excel in these areas will ultimately bring the most value and have the most impact, as the world becomes AI-empowered.