Iterative Research: Human-Guided Exploration
The power of ResearchCrew is in iteration. After each round, you review findings and actively steer the next research direction.
The Iterative Loop
Round 1: Initial Research
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Review findings
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Identify gaps and areas to explore
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Round 2: Guided Research (with feedback)
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Review refined findings
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Request deeper analysis or new angles
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Round 3+: Continue iterating
Step-by-Step Example
Round 1: Initial Exploration
Create input.md:
# Research Topic: The Future of Remote Work
Research the future of remote work post-2025. Include current trends,
company policies, and predictions from industry experts.
Run:
You get <yyyymmdd>.md with initial findings on remote work trends.
Round 2: Provide Feedback & New Direction
After reviewing <yyyymmdd>.md, add feedback at the end:
## User Feedback
Good coverage on company policies and trends. Now explore:
1. **Mental health impact** — Research psychological effects of remote work
(isolation, burnout, work-life balance)
2. **Hybrid model adoption** — Focus on companies successfully implementing
hybrid (not full remote) models
3. **Tools & infrastructure** — Deep dive into remote work tools and technologies
Please de-prioritize:
- General history of remote work (already covered)
- COVID-era temporary remote mandates (focus on permanent structures)
Run again:
The crew:
- Remembers your previous research
- Sees your feedback
- Explores the new topics you highlighted
- Skips areas you said to de-prioritize
- Integrates findings from both rounds
You get an updated <yyyymmdd>.md with deeper analysis on your chosen angles.
Round 3: Refine Further
Review the updated report. You notice:
- Mental health section is strong
- Hybrid model section needs more company case studies
- Tools section could include budget considerations
Add new feedback:
## User Feedback
Excellent analysis on mental health. The hybrid model section is good but needs:
- Real case studies (which companies, what results)
- Employee satisfaction metrics
- Implementation challenges and solutions
For tools section, add:
- Cost comparisons between platforms
- SMB vs Enterprise tool choices
- Security and compliance considerations
New area to explore:
- How remote work is affecting real estate and office spaces
Run again:
Continue until the research meets your needs.
How the Crew Remembers
ResearchCrew uses LanceDB to persist memory across runs:
What it remembers:
- Previous research outputs
- Your input topic
- Extracted claims and sources
- URLs already crawled
What it learns:
- Your feedback patterns (what you want deeper on)
- Topics you want to skip
- The full context of prior investigation
What it avoids:
- Re-crawling already-researched URLs
- Repeating topics you've marked as complete
- Redundant searches
This means each iteration builds on prior work rather than starting fresh.
Feedback Format
Types of Feedback You Can Provide
1. Explore deeper:
## User Feedback
The AI ethics section needs more depth:
- Current regulatory approaches (EU, US, etc.)
- Industry self-regulation vs government mandates
- Specific AI governance frameworks
2. New topic area:
## User Feedback
Also research:
- Supply chain risks of outsourced AI
- Labor implications of AI automation
3. De-prioritize topics:
## User Feedback
Good coverage on: history of AI
Skip further research on:
- Ancient history of computing
- Academic theoretical frameworks (unless directly relevant)
4. Fix inaccuracies:
## User Feedback
The report claims "AI adoption is 50% complete" but this seems high.
Please verify this with recent enterprise adoption surveys.
Also, the section on "AI healthcare applications" missed regulatory barriers.
5. Change focus:
## User Feedback
The report is too academic. Focus more on:
- Practical use cases
- Real-world adoption barriers
- Implementation timelines
De-prioritize:
- Theoretical frameworks
- Historical background
Feedback Best Practices
Good feedback:
- Specific (name the topic or section)
- Action-oriented (what to explore)
- Clear scope (what to skip)
Vague feedback:
- "Make it better"
- "More research"
- "I didn't like the AI section"
Better: "The AI section focuses on hype. Please research actual production deployments, success rates, and lessons learned from real implementations."
Multi-Day Research
ResearchCrew remembers context across sessions:
Day 1:
Review, provide feedback.
Day 2:
The crew has full history from Day 1, so it knows what was researched and what feedback you gave.
You can distribute feedback and runs across days without losing context.
When Iteration Helps Most
Use iterative research when:
- Topic is complex and requires multiple angles
- You want to steer direction based on findings
- Quality is more important than speed
- You're building a comprehensive report
Use single-round when:
- Simple, focused questions ("What is X?")
- You just need quick facts
- Topic doesn't require refinement
Optimization Tips
Efficient Iterations
Each iteration is a new full pipeline run. To speed things up:
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Batch feedback — Collect multiple feedback items before next run, rather than running after each small note
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Specific topics — Name specific sections/topics to explore, so the crew doesn't re-research completed areas
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Quality over quantity — 2-3 focused iterations produce better results than 10 scattered ones
Iteration Limit
A practical approach:
- Round 1: Initial exploration (comprehensive)
- Round 2: Address major gaps (targeted)
- Round 3: Polish and verify (refinement)
- Round 4+: Diminishing returns (fine-tuning)
Most research reaches diminishing returns after 3-4 rounds.
Exporting & Sharing
After you're satisfied with your research:
The report includes full citations, so others can:
- Read your findings
- Verify sources by clicking links
- Trace claims back to original sources
Next Steps
- Configuration — Optimize LLM choices and performance
- Examples — See full multi-round workflow examples
- Architecture — Understand how feedback is incorporated