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š“āā ļø The 7-Step Workflow That Beats AI Research
Donāt Trust One Prompt
Captain YAR
February 14, 2026 ā¢ Estimated Reading Time: 9 minutes
The last time I trusted a āfully automatedā AI research agent, I almost walked into a bad decision. Not because the tool was dumb. Because it was confident. It stitched together half-truths, skipped key definitions, and gave me something that looked polished enough to believe. Thatās why this creatorās post hit me in the gut. A talented Reddit user, Electronic_Home5086, basically says: if the stakes are high, full automation is the wrong goal.
The point is simple, but it changes how you work: AI speed plus human judgment beats āset it and forget itā every time accuracy matters. The workflow is built on recursive verification ideas, including MIT-style thinking around checking your work in loops instead of one big leap. And itās not tied to any single platform. You can run it with ChatGPT, Claude, Perplexity, or whatever tools you already use.
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Recursive Verification
AI tends to break when you ask it to do everything in one prompt. If you say, āResearch EV batteries and write a report,ā it will compress messy reality into a clean story, and that is where hallucinations and missing nuance sneak in. The fix is to split the job into phases, and verify the output before it moves forward.
This creatorās seven phases are: Building a Map, Collecting Evidence, Deep Diving, Checking Quality, Writing the Report, Stress Testing, and Polishing. The underrated move is switching models by phase. Use reasoning models for planning and logic, and faster retrieval models for gathering sources, then you manually approve what gets promoted into the next step.
The Project Manager Phase Is the Make or Break Step
Most people start with questions. This workflow starts with structure. In the āDecompositionā phase, you ask the AI to act like a project manager and break your objective into 6 to 8 sub-questions with clear dependencies.
That dependency part is everything. You cannot estimate āmarket sizeā until you define what market you mean, who the target user is, and what counts as a competitor. When the AI lists information requirements and source types for each sub-question, you get a real roadmap, not a wandering brainstorm. You then fix the map while itās cheap, instead of fixing the report when itās late.
IParallel Evidence Collection With Hard Constraints
Once the map is approved, you switch gears. This is not the moment for deep thinking. Itās the moment for fast, careful retrieval, ideally in parallel threads that each tackle a single sub-question.
The key constraint in this phase is forcing citations and a credibility check for each source. Instead of a fluffy summary, you get bullet-point findings that are anchored to origins. Then you do the most important human job in the whole system: you pick what is valid, relevant, and clean enough to carry forward. Only verified points get promoted into the shared context for writing.
The Adversarial Stress Test
A lot of people stop when the draft looks good. This workflow treats that as the danger zone. Before you finalize anything, you feed the draft to a different model and tell it to be harsh.
You ask it to find weak links, missing counterarguments, and logical leaps. Itās basically peer review, but faster and less emotional. Switching models matters because it reduces the echo effect where one system nods along with its own framing. The creator notes this step often catches contradictions a tired human eye will miss, and that alone can save you from embarrassment or costly mistakes.
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Prompt of the Day: The Decomposition Planner
Use this with a reasoning model, and treat the output like a draft plan, not truth.
Research Objective: [Your main question ā be specific]
Context:
ā Purpose: [Why you need this ā investment decision, product strategy, etc.]
ā Scope: [Geographic region, time period, constraints, or āno constraintsā]
ā Depth needed: [Surface overview / Moderate / Deep analysis]
ā Key stakeholders: [Who will use this, or ājust for meā]
Task: Create a comprehensive research plan
Break this into 6-8 sub-questions that together fully answer the objective. For each:
1. Specific information requirements (data, expert opinions, case studies, etc.)
2. Likely authoritative sources (academic papers, industry reports, government data, etc.)
3. Dependencies (which questions must be answered before others ā be explicit)
4. Search difficulty (easy/moderate/hard)
5. Priority ranking (1-8, with 1 being highest)
Output format:
ā Numbered list of sub-questions
ā For each: [Info needed] | [Source types] | [Dependencies] | [Difficulty] | [Priority]
ā Final section: Recommended research sequence based on dependencies
If you want to master this workflow, I highly recommend looking at the original post for the full breakdown of all seven phases.
Check out the full post by Electronic_Home5086 on Reddit.
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