Deep research glossary
What is deep research?
Deep research is an AI-assisted research process for decisions where a quick answer is not enough. It searches for evidence, compares sources, checks freshness, challenges assumptions, and writes a structured synthesis.
Why deep research is different from normal AI research
Normal AI research is often a single conversational answer or a short search session. That can work for simple facts, but it can miss source conflicts, stale evidence, edge cases, and trade-offs.
Deep research uses multiple steps: focused search, source comparison, adversarial review, and synthesis. That makes it better suited for architecture choices, vendor comparisons, buying decisions, policy questions, and other topics where the answer should be reusable and evidence-backed.
It searches with intent
Deep research breaks a broad decision into focused angles, so the system can look for official docs, comparisons, risks, freshness signals, and dissenting evidence separately.
It challenges the answer
Reviewer passes pressure-test the findings before synthesis, reducing unsupported claims and one-sided recommendations.
It is worth caching
A good deep research report can take meaningful time and effort to produce. Caching turns that work into a reusable decision asset.
Deep research glossary
- Deep research
- A multi-step AI research workflow that plans a topic, searches for evidence, compares sources, challenges weak claims, and synthesizes a structured report instead of answering from one prompt.
- Normal AI research
- A single conversational answer or a small number of searches. It can be fast, but it often misses source conflicts, hidden assumptions, freshness issues, and decision caveats.
- Source-backed synthesis
- A report that connects recommendations to supporting sources and makes trade-offs explicit, rather than returning a fluent but unsupported summary.
- Adversarial review
- A critique pass that looks for unsupported claims, missing edge cases, stale evidence, or one-sided conclusions before the final report is written.
- Cached deep research
- A reusable archive of completed deep research reports so similar future questions can start from existing work instead of paying time and search costs again.
When to use deep research
Use deep research when the answer needs evidence, caveats, source freshness, or a reusable decision record. For simple facts, a normal search or a short AI answer is usually enough.
More resources
- All resources
Browse the cached deep research resource library. - Deep research glossary
What deep research means, how it differs from normal AI research, and when a source-backed synthesis is worth the extra time. - Reuse and cache deep research
How saved deep research reports help people reuse source-backed work instead of starting over. - Deep research cost
How source gathering, review depth, and synthesis affect the cost of a live deep research report. - Deep research vs AI search
When a quick AI search is enough and when multi-step research, review, and synthesis are more useful. - TESRAC features
A feature overview for TESRAC's cached deep research workflow.