Deep Research
Systematic methodology for conducting exhaustive, accurate research using all available tools. Prioritizes correctness over speed.
Core Principles
- •Multiple sources required — Never rely on a single source for important claims
- •Cross-reference everything — Verify facts appear consistently across independent sources
- •Citation mandatory — Every claim must have a source; no unsourced assertions
- •Acknowledge uncertainty — When sources conflict or are weak, say so explicitly
- •Prefer primary sources — Official docs > blog posts > forum answers > AI-generated content
Available Research Tools
Use these tools in combination based on the research topic:
| Tool | Best For | Limitations |
|---|---|---|
| WebSearch | Current events, recent information, broad topic discovery | Results may be outdated, SEO-influenced |
| WebFetch | Reading specific URLs, extracting detailed content | Requires known URL |
| Playwright browser | Interactive sites, paywalled content (if logged in), complex navigation | Slower, requires more tokens |
| Context7/MCP docs | Library/framework documentation | Only indexed libraries |
| OpenAI docs MCP | OpenAI API specifics | OpenAI only |
| Grep/Glob/Read | Codebase research, finding implementations | Local files only |
Research Workflow
Phase 1: Scope Definition
Before researching, clarify:
- •Core question — What specific question(s) need answering?
- •Required depth — Surface overview or exhaustive deep-dive?
- •Recency requirements — Is timeliness critical? (API versions, current events, etc.)
- •Authoritative sources — What would count as a definitive answer?
Ask clarifying questions if scope is ambiguous. Use AskUserQuestion for structured choices when multiple research directions are possible.
Phase 2: Source Discovery
Cast a wide net to find relevant sources:
1. WebSearch with multiple query variations - Try 3-5 different phrasings of the core question - Include technical terms AND plain language - Search for "[topic] official documentation" - Search for "[topic] research paper" or "[topic] study" 2. Identify authoritative sources from results - Official documentation sites - Academic papers / research institutions - Industry standards bodies - Recognized experts in the field 3. Check specialized tools - Context7 for library/framework docs - OpenAI docs MCP for OpenAI-specific topics - GitHub/codebase for implementation details
Source discovery heuristics:
- •Government and academic domains (.gov, .edu, .ac.uk) tend toward accuracy
- •Official project documentation is authoritative for that project
- •Wikipedia is a starting point, not an endpoint — follow its citations
- •Stack Overflow answers need verification; check votes and dates
- •Be skeptical of content farms and SEO-optimized listicles
Phase 3: Deep Reading
For each promising source:
- •Fetch full content — Use WebFetch or browser to get complete text
- •Extract key claims — Note specific facts, figures, dates, quotes
- •Note source metadata — Author, date, organization, potential biases
- •Identify citations — What sources does this source cite?
- •Flag conflicts — Does this contradict other sources?
Reading strategy for different source types:
| Source Type | Strategy |
|---|---|
| Documentation | Read relevant sections fully; note version/date |
| Research paper | Abstract, conclusion, methodology in that order |
| News article | Check publication date, author credentials, cited sources |
| Blog post | Verify claims independently; note author's expertise |
| Forum/Q&A | Check answer date, votes, accepted status; verify independently |
Phase 4: Cross-Verification
For each major claim:
- •Find 2+ independent sources — Sources that don't cite each other
- •Check for conflicts — Note any disagreements between sources
- •Prefer newer sources — For rapidly evolving topics
- •Weight by authority — Primary sources > secondary > tertiary
Conflict resolution:
- •When sources disagree, report all positions with citations
- •Investigate why they disagree (different contexts, outdated info, different definitions)
- •If one source is clearly more authoritative, note that
- •Never silently pick one version
Phase 5: Synthesis & Output
Structure findings clearly:
## Research Summary: [Topic] ### Key Findings 1. **[Finding 1]** - [Specific fact with citation] - [Supporting evidence] - Confidence: High/Medium/Low - Sources: [1], [2] 2. **[Finding 2]** ... ### Conflicts & Uncertainties - [Area of disagreement]: Source A claims X [1], while Source B claims Y [2]. [Analysis of why they differ] ### Source Quality Assessment | # | Source | Type | Authority | Recency | Notes | |---|--------|------|-----------|---------|-------| | 1 | [URL] | Official docs | High | 2024-01 | Primary source | | 2 | [URL] | Research paper | High | 2023-06 | Peer-reviewed | | 3 | [URL] | Blog | Medium | 2024-03 | Author is [expert] | ### Gaps & Limitations - [What couldn't be verified] - [Areas needing more research] ### Citations [1] [Full citation with URL] [2] [Full citation with URL] ...
Confidence Levels
Assign confidence to each finding:
| Level | Criteria |
|---|---|
| High | 3+ independent authoritative sources agree; no conflicts |
| Medium | 2 sources agree, or 1 highly authoritative source; minor conflicts |
| Low | Single source, or significant conflicts between sources |
| Uncertain | Sources conflict significantly; unable to determine truth |
Always state confidence explicitly. "I'm not sure" is a valid research finding.
Citation Format
Use inline citations with numbered references:
The API rate limit is 60 requests per minute [1], though this can be increased for enterprise accounts [2]. --- [1] OpenAI API Documentation, "Rate Limits", https://platform.openai.com/docs/guides/rate-limits, accessed 2024-01-15 [2] OpenAI Enterprise FAQ, https://openai.com/enterprise, accessed 2024-01-15
Citation must include:
- •Source name/title
- •URL (if web source)
- •Access date (for web sources)
- •Publication date (if available)
Special Research Scenarios
Rapidly Evolving Topics (AI, crypto, etc.)
- •Prioritize sources from last 6 months
- •Check official changelogs and release notes
- •Note when information might be outdated
- •Consider using browser to check current state directly
Controversial Topics
- •Present multiple perspectives with citations for each
- •Identify the strongest arguments on each side
- •Note which sources might have biases and why
- •Don't pick sides unless evidence is overwhelming
Technical Implementation Questions
- •Check official documentation first (Context7, MCP servers)
- •Look for example code in GitHub
- •Verify against actual behavior if possible
- •Note version-specific differences
Comparative Research ("X vs Y")
- •Use same evaluation criteria for all options
- •Find sources that compare directly when possible
- •Check for bias (vendor-sponsored comparisons)
- •Note what each option is optimized for
Anti-Patterns to Avoid
| Anti-Pattern | Why It's Bad | Instead |
|---|---|---|
| Single source | No verification | Always find 2+ sources |
| Uncited claims | Unverifiable | Every fact needs a source |
| Assuming first result is best | SEO != accuracy | Evaluate source quality |
| Ignoring conflicts | Hides uncertainty | Report all positions |
| Outdated sources | Information decay | Check publication dates |
| Trusting AI summaries | May hallucinate | Go to primary sources |
| Stopping early | Incomplete picture | Research until diminishing returns |
Completion Criteria
Research is complete when:
- •Core question(s) answered with citations
- •Key claims verified by 2+ independent sources
- •Conflicts and uncertainties explicitly noted
- •Source quality assessed for all citations
- •Confidence levels assigned to findings
- •Gaps and limitations documented
Reference Files
For detailed guidance on specific scenarios:
- •Source Evaluation Criteria — How to assess source reliability
- •Search Strategies — Advanced query techniques for different domains