Prompt Engineering peaked between 2022 and 2024 as the foundational skill for working with large language models. It focused on crafting precise instructions such as roles, few-shot examples, and structured phrasing to get the best possible output in a single interaction. The model was treated as a black box, and success depended on how well you asked. Context Engineering emerged in 2025 and expanded the scope beyond the prompt. It focused on curating everything the model sees inside the context window. This includes retrieval systems, memory, tool outputs, summaries, and smart context management. Prompt engineering became just one part of a larger system designed to ensure the model always has the right information at the right time. Now in 2026, the frontier is Harness Engineering. The shift is simple but profound: Agent = Model + Harness Harness Engineering is about designing the system around the model. It turns a powerful but unpredictable LLM into a reliable, production-grade agent. Instead of relying on better prompts or more context, it builds structure, constraints, and feedback loops that guide the model’s behavior. Think of it like managing a junior engineer. You do not just give instructions. You define boundaries, provide tools, enforce standards, and create systems that prevent repeated mistakes. This shift happened because capability is no longer the bottleneck. Reliability is. Even the most advanced models still drift, hallucinate, and repeat errors. Context alone cannot solve long-running or multi-session workflows. The real leverage comes from engineering the environment in which the model operates. A strong harness typically includes six layers: 1. Tool and Permission Layer Clearly defined actions, APIs, and boundaries the agent can access. 2. State and Memory Management Persistent logs, checkpoints, and artifacts that survive across sessions. 3. Context and Prompt Orchestration Dynamic and structured context strategies supported by versioned documentation.

Tell us three things: 1. What you do (job, industry, student, career-changer, whatever) 2. What brought you to Clief Notes 3. One thing you're trying to figure out right now related to computing or AI I'll respond to every single one. And read each other's intros too because the person who's stuck on the same problem as you might already be in this thread. I'll go first I am Jake, I have been working in tech for 15 Years, building with Generative AI for 3 Years straight now! Excited to teach and learn! That's it. Simple, scannable, gives you data on who's joining and what they need, and keeps the feed clear for content that retains people past week one.