Platform & Edge Engineer

Small Language Models (SLMs) are compact neural networks designed to perform language tasks locally, on-edge, or with minimal compute resources compared to traditional Large Language Models (LLMs). Unlike massive models (GPT-4, Claude 3 Opus) which pass 1 Trillion parameters, SLMs typically range from 1B to 8B parameters (e.g., Llama 3 8B, Phi-3, Gemma, Mistral). They sacrifice broad general knowledge but maintain extremely high reasoning capabilities. **Why they matter in 2025/2026:** SLMs solve the AI margin collapse problem. Because they are 10-50x cheaper to run, organizations are aggressively routing routine tasks to SLMs while reserving expensive LLMs only for highly complex cognitive routing.

Open Weights refers to AI models where the trained parameters (weights) are made publicly available for download and execution, but the underlying training data and training code are kept proprietary. In 2025/2026, the technology industry shifted away from calling models like Llama or Mistral "Open Source" (which legally requires the training data to be public per the OSI definition) and adopted "Open Weights" as the technically accurate term. Open weights democratize AI inference, allowing any company to download, self-host, and fine-tune frontier-class models securely within their own VPCs without sending sensitive data to third-party endpoints.

Developer Experience encompasses the tools, workflows, processes, and environment that affect how productive and satisfied software developers are in their daily work. Good DevEx means developers spend most of their time on creative, high-value work. Bad DevEx means they fight tools, wait for builds, and navigate bureaucracy. Key DevEx dimensions (Nicole Forsgren's framework): feedback loops (how quickly developers get results from their actions), cognitive load (how much complexity developers must hold in their heads), and flow state (how often developers achieve deep, uninterrupted focus). DevEx investments include: fast CI/CD pipelines (<10 min builds), good documentation, reliable dev environments, automated testing, clear code review processes, and minimal context-switching. DevEx directly impacts retention. Developer Experience surveys consistently show that engineers leave companies primarily because of poor tools and processes, not because of compensation.

Retrieval-Augmented Generation (RAG) is an AI architecture pattern that combines a language model with a knowledge retrieval system. Instead of relying solely on the model's training data, RAG retrieves relevant documents from a knowledge base and includes them in the prompt, grounding the AI's responses in specific, verifiable information. RAG reduces hallucinations by giving the model factual context to work with. It's the most popular enterprise AI pattern in 2026 because it allows organizations to use their proprietary data with general-purpose language models without fine-tuning. The economics of RAG involve balancing retrieval costs (vector database queries, embedding generation) against the cost of hallucination and the alternative cost of fine-tuning. For most enterprise use cases, RAG is significantly cheaper than fine-tuning while providing better accuracy on domain-specific questions.