Fine-tuning creates model-specific optimizations that quickly become obsolete. Blitzy favors developing sophisticated, system-level "memory" that captures enterprise-specific context and preferences. This approach is model-agnostic and more durable as base models improve, unlike fine-tuning which requires constant rework.

Structure AI context into three layers: a short global file for universal preferences, project-specific files for domain rules, and an indexed library of modular context files (e.g., business details) that the AI only loads when relevant, preventing context window bloat.

Connecting to a design system is insufficient. AI design tools gain true power by using the entire production codebase as context. This leverages years of embedded decisions, patterns, and "tribal knowledge" that design systems alone cannot capture.

The early focus on crafting the perfect prompt is obsolete. Sophisticated AI interaction is now about 'context engineering': architecting the entire environment by providing models with the right tools, data, and retrieval mechanisms to guide their reasoning process effectively.

Even models with million-token context windows suffer from "context rot" when overloaded with information. Performance degrades as the model struggles to find the signal in the noise. Effective context engineering requires precision, packing the window with only the exact data needed.

Relying solely on semantic clustering (RAG) is inaccurate for complex domains like code. Blitzy combines a deep, relational knowledge graph with semantic understanding to accurately retrieve context, using the semantic match as a map to the source of truth rather than the truth itself.

Overloading LLMs with excessive context degrades performance, a phenomenon known as 'context rot'. Claude Skills address this by loading context only when relevant to a specific task. This laser-focused approach improves accuracy and avoids the performance degradation seen in broader project-level contexts.

The next IDE evolution will transform the codebase into a dynamic 'metadata backbone'. By capturing a continuous history of edits and conversations, it will allow all context—discussions, decisions, feedback—to be permanently anchored to specific lines of code, unlike today's static, snapshot-based Git workflows.

Static analysis isn't enough to understand a complex application. Blitzy's onboarding involves spinning up and running a parallel instance of the client's app. This process uncovers hidden runtime dependencies and behaviors, creating a far more accurate knowledge graph than code analysis alone could provide.