Prompt and Context Engineering: Why the Difference Matters

Prompt and context engineering are often discussed as interchangeable skills. In a recent blog post, eDiscovery Today founder Doug Austin argues that they resolve fundamentally different problems. Prompt engineering drives tasks like summarization or classification, while context engineering defines the legal, factual, and procedural boundaries that make those tasks defensible. 

Prompt engineering is what you ask AI in a single interaction. It’s about providing carefully worded instructions to control output format and content. Context engineering is everything you give AI before it responds. This includes supplying background materials, case facts, governing rules, and relevant prior history to guide AI with the right evidence so that the response is defensible. 

An example of context engineering in eDiscovery is providing a discovery order, the ESI (Electronically Stored Information) protocol, prior rulings, the review workflow, custodian, and data source lists before asking AI to identify risks or recommend next steps. The result is not just more detailed output, but output that aligns with legal realities and procedural constraints.

The same distinction between prompt and context engineering applies to various use cases, including custodian interviews, early case assessment, privilege review, technology-assisted review (TAR), workflows, and meet-confer drafting. Prompt engineering asks AI to perform a task. With context engineering, the output becomes targeted, relevant, and more likely to withstand scrutiny.

Austin ends the blog post with a measured takeaway. Context engineering does not eliminate hallucinations or the need for quality control, but it meaningfully improves relevance and usefulness. The end result is a response that is more targeted, provides deeper insights, and creates a more defensible output.