Five-Part Deep Research Brief
Brief deep research with context, assignment, metrics, scope, and deliverable.
- Difficulty
- Moderate
- Time to result
- ~days to results
- Steps
- 7
- Confidence
- 99%
Construct a deep-research request from five parts. First, establish context such as the user’s role, company size, industry, customers, technology stack, goals, and challenges. Second, define a clear assignment describing the question the research must answer. Third, state objectives and measurable criteria so evidence can be evaluated against the intended decision. Fourth, bound the work with scope and priorities, including required integrations, markets, sources, exclusions, or benchmarks. Fifth, prescribe the deliverable format that best supports the next action, whether that is a report, comparison, memo, or input package for another prompt. Clarifying questions should resolve missing variables before the expensive research run begins. This structure improves relevance by connecting evidence collection directly to a specific operating context and output.
Origin
Extracted from Marketing Against The Grain as the hosts’ reusable template for obtaining stronger results from AI deep-research systems.
Core principles
- 01Research quality depends on the situation surrounding the question.
- 02A clear assignment defines what the research must resolve.
- 03Objectives and metrics turn information gathering into decision support.
- 04Scope and priorities prevent unfocused coverage.
- 05The deliverable should be designed for its downstream use.
How to run it
- 1
Set the context
Describe who is requesting the research and the environment in which the answer will be used. Include role, company, audience, stack, goals, and challenges when relevant.
Pro tip Prioritize context that could change the recommendation.
Watch out Generic background that does not affect the analysis adds noise.
- 2
State the assignment
Define the research question and the decision or artifact it must support. Explain what kind of answer will be useful.
Pro tip Frame the assignment around a decision rather than a topic.
Watch out “Tell me about this market” is too broad for focused research.
- 3
Define objectives and metrics
List the desired outcomes and the criteria used to evaluate findings. Include measurable targets where available.
Pro tip Distinguish mandatory thresholds from desirable qualities.
Watch out Metrics without a decision context can encourage superficial ranking.
- 4
Bound scope and priorities
Specify the markets, time horizon, products, integrations, sources, exclusions, and ranked concerns that define the investigation. Make trade-offs explicit.
Pro tip State which requirements are non-negotiable.
Watch out An unbounded scope produces breadth at the expense of actionable depth.
- 5
Design the deliverable
Tell the research system exactly how to package its findings for the next use. Request sections, tables, citations, recommendations, or machine-readable fields as needed.
Pro tip Match the output to the downstream prompt or business process.
Watch out A generic report may require another lossy transformation before it becomes useful.
- 6
Resolve clarifications
Answer the system’s pre-run questions to close important information gaps. Correct any assumptions embedded in those questions.
Pro tip Use clarifications to tighten scope before consuming a research run.
Watch out Rushing past clarification can waste the run on the wrong problem.
- 7
Audit the result
Check citations, cases, benchmarks, and claimed integrations against the original requirements. Separate well-supported conclusions from uncertain findings.
Pro tip Ask for evidence adjacent to important recommendations.
Watch out A polished report can still rely on weak or mismatched evidence.
In the wild
A content or sales leader provides company size, target customers, existing HubSpot usage, and the goal of improving cold-email open rates. The brief requires a HubSpot integration, prioritizes relevant case studies and performance benchmarks, and asks for a comparison report with a recommendation.
→ The research concentrates on viable tools and evidence connected to the team’s actual objective.
A content manager at a 200-person SaaS company explains that the team uses HubSpot and Jasper and wants to scale content and audience growth. The assignment, metrics, priorities, and report format are then tailored to that operating context.
→ Recommendations reflect the company’s stack and growth goals rather than generic tool popularity.
Common mistakes
Requesting a pile of information
A broad topic request does not tell the system which decision, metric, or priority should shape the research.
Under-specifying the requester
Without role, company, customers, and relevant stack details, recommendations default to generic assumptions.
Deferring the deliverable design
Specify the downstream format during research rather than reprompting an unstructured report afterward.
Is it for you?
Best for
It is best for strategic reports, vendor evaluations, market analysis, and research that will feed another business artifact.
Not ideal for
It is excessive for simple factual lookups that have one clear answer and little contextual variation.
From the transcript
“The five core components of a deep research prompt is like set the context, make sure you have clarity on the assignment and it's really…”
“And so what I found is if you set the context about who you are, you know, the company you're in, all of the things…”
“Deep research now, just so everybody knows, it's always gonna ask clarifying questions.”
From the episode
Use This AI Trick To Get 10x Better Results Every Time