From Prompt Engineering to Context Engineering
Why modern AI applications depend less on clever prompts and more on retrieval, memory, tool design, evaluation, and context quality.
Prompts are interfaces
A prompt is the model-facing interface for a product workflow. It matters, but it cannot compensate for missing data, vague permissions, or weak evaluation.
Context is the product surface
High-quality context includes verified documents, user intent, product state, conversation history, and tool outputs. The model should see only what helps the next step.
Build retrieval intentionally
Chunk content around user tasks, not arbitrary token lengths. Store source metadata, freshness, ownership, and confidence so the answer can explain itself.
Evaluate the whole path
Measure groundedness, answer usefulness, latency, and cost together. A beautiful answer that ignores policy or uses stale context is still a failed answer.
Frequently asked questions
Is prompt engineering still useful?
Yes, but it is now one part of a broader system that includes context retrieval, evaluation, and tool constraints.
Author
Maya Chen
Maya covers applied AI, automation, and responsible product strategy for technical teams.
