Sensei FAQs

Question 1

Does Sensei integrate with existing development workflows?

Accepted Answer

Yes, Sensei is designed for seamless integration. It supports comprehensive third-party MCP integration, offers CI/CD workflows for GitHub Actions and GitLab CI, and includes features for team collaboration, session merging, and exporting architectural decision records (ADRs).

Question 2

What is Sensei and how does it improve LLM reasoning?

Accepted Answer

Sensei is a Multi-Context Provider (MCP) tool that injects engineering standards, architectural decisions, and specialized AI persona guidance directly into LLM reasoning processes *before* the LLM generates a response. This ensures LLM outputs are proactively aligned with your project's guidelines.

Question 3

What is the key advantage of Sensei's approach over traditional LLM prompts?

Accepted Answer

Unlike traditional output styles or custom instructions applied *after* an LLM has already reasoned, Sensei actively injects standards and context *before* reasoning occurs. This fundamental difference ensures that the LLM's core logic is influenced by your guidelines, leading to truly standards-compliant and architecturally sound code.

Question 4

How many AI personas does Sensei offer and what do they do?

Accepted Answer

Sensei orchestrates 64 specialized AI personas, covering a wide range of engineering domains like API Platform Engineer, Security Sentinel, Database Architect, and more. These personas provide multi-perspective guidance, allowing LLMs to reason with expert knowledge from diverse fields.

Question 5

What kind of engineering standards and context can Sensei inject?

Accepted Answer

Sensei can inject context based on over 50 file types (e.g., Python, JS/TS, Go), operation context (CREATE, REFACTOR, DEBUG, SECURITY), specific keywords (multi-tenant, payment), and maintains session memory of past architectural decisions to ensure consistency.

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