What future trends do you foresee in prompt engineering, and how are you preparing to adapt to them?

Prompt engineering is moving from clever wording towards engineering systems: deciding what context a model sees, wiring it to tools, constraining its outputs and measuring results with evaluations. I prepare by building those skills, not by collecting prompt tricks.

Trends that are already under way

1. Context engineering For agents that work over many steps, the hard part is choosing what goes into the context window at each step: retrieved documents, tool results, memory and summaries. Anthropic describes this as context engineering, the successor to writing a single prompt.

2. Reasoning models Models that think before answering need less instruction on how to reason. Their makers advise short, direct prompts and say step-by-step instructions are unnecessary or can hurt. Prompting shifts to stating goals, constraints and what good output looks like.

3. Agents and tool use Models increasingly call APIs, run code and act on systems, in loops of reasoning and action (the ReAct pattern). Prompting now includes writing tool descriptions, setting permissions and deciding when a person must approve an action. Standards such as the Model Context Protocol make tools reusable across applications.

4. Structured outputs APIs can force answers to match a JSON schema, so format instructions and parsing workarounds matter less, and model outputs plug directly into software.

5. Automatic prompt optimisation Frameworks such as DSPy, and methods where a model proposes and scores its own instructions, tune prompts against a metric. The human role moves to defining the task, the data and the metric.

6. Evaluation as the core skill As models change every few months, the lasting asset is a good test set and grading method that shows whether a change helped.

7. Multimodal inputs Prompts increasingly mix text with images, audio, documents and screen content.

8. Security Prompt injection, especially through documents, web pages and tool results, is a central design concern for agents, with defences at the application level.

How I'm preparing

• Building evaluation sets and graders for every project, and treating prompts as versioned code.
• Learning retrieval, embeddings and agent frameworks, not just prompt wording.
• Practising with structured outputs and tool definitions.
• Reading makers' prompting guides when new model generations arrive, since advice changes.
• Following security guidance such as the OWASP list for LLM applications.
• Keeping the fundamentals: clear writing, understanding the domain, and knowing what a good answer looks like. Those don't go out of date.