Prompting, Retrieval & Agents

Even the most capable AI model is only as useful as the way you interact with it. This category covers the practical techniques that sit between a powerful model and a useful application - the methods for getting better outputs, connecting models to your data, and enabling AI systems to take action in the real world. Prompt engineering is the craft of asking better questions. Retrieval-augmented generation lets models work with information they weren't trained on. Agents give models the ability to use tools, access external systems, and complete multi-step tasks autonomously. These techniques are where most of the practical value creation happens today, because they don't require training your own model - they work with existing models via their APIs. For most businesses, investing in better prompting, retrieval and agentic workflows will deliver more immediate value than fine-tuning or training custom models. The flip side is that these techniques have their own complexities and failure modes. A poorly designed retrieval system can feed irrelevant information to the model. An agent without proper guardrails can take unintended actions. Understanding these methods helps you build AI applications that are genuinely reliable rather than merely impressive in demos.