Advanced Reasoning

Reasoning, not just retrieval.

For questions that require more than a quick search, the assistant pulls together multiple sources and reasons through complex technical problems.

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Incident post-mortem
Claude 3.5 Sonnet
Why did the authentication service crash yesterday at 14:00 UTC? Pull together all related PRs, Jira tickets, and Slack conversations.
Analyzing 14 Slack threads from #eng-auth...
Cross-referencing with Jira ticket AUTH-902...
Reviewing code diffs in PR #1142...
The crash at 14:00 UTC was caused by a database connection pool exhaustion introduced in PR #1142.

According to Jira ticket AUTH-902, the goal was to increase token verification speed. However, as noted by E. Roberts in #eng-auth at 14:05 UTC, the new connection logic failed to release idle connections back to the pool. When traffic spiked during the marketing launch, the pool hit its maximum limit of 100 concurrent connections, leading to the outage.
Synthesized Sources
PR #1142Connection logic
AUTH-902Token speed
#eng-authOutage thread

Connecting the dots. Move beyond finding single files. Ask questions that require traversing multiple systems to formulate a complete answer.

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Multi-source synthesis

The assistant reads across your tools simultaneously. It maps a discussion in Slack directly to the code changes in GitHub and the task in Jira.

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Model selection

Choose the right brain for the job. Switch to advanced models like Claude 3.5 Sonnet mid-conversation when a question demands complex logical reasoning.

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Thorough analysis

Deep research prioritizes completeness over immediate speed. The assistant systematically uncovers related documents until the full context is gathered.

Beyond semantic search

Finding the right documents is only the first step. True architectural intelligence requires an engine that can read those documents, extract the relevant logic, and synthesize a cohesive conclusion.

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Questions and answers

Common questions about deep research.

Deep research is ideal for complex, multi-layered questions that cannot be answered by a single document. Use it when you need to diagnose an incident across multiple microservices, trace a feature's history, or understand a sprawling architectural decision.

Yes. You can switch between major LLM providers mid-conversation. Use faster models for quick semantic retrieval, and switch to reasoning-heavy models (like Claude 3.5 Sonnet) for deep research.

Yes. Deep research prioritizes thoroughness over immediate speed. The assistant systematically queries multiple sources, reads them, and synthesizes the findings before returning a response.

If the assistant finds conflicting information across different sources (e.g., a Jira ticket says one thing, but the codebase shows another), it explicitly highlights the discrepancy and cites both sources so you can make an informed decision.

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