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WebSearch	Current events, recent information, broad topic discovery	Results may be outdated, SEO-influenced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

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