Manus Knowledge Feature Information

What is the Knowledge Feature?

The Knowledge feature in Manus.im allows users to input and manage custom contextual information that the AI agent can reference to improve task execution. This feature is designed to tailor the AI's responses and actions to your specific needs, ensuring it aligns with your workflows, industry jargon, or proprietary data.

Purpose

Store domain-specific information, guidelines, or preferences that Manus.ai should use when performing tasks.

Key Fields

  1. Name: A unique identifier for the knowledge entry (e.g., "Product Glossary" or "Customer Support Protocols").
  2. When to Use: Define triggers or scenarios where this knowledge applies (e.g., "Use during customer service ticket resolution").
  3. Content: The actual information (text, links, structured data) the AI should reference.
  4. Status: Toggle to enable/disable the knowledge entry.

Best Practices

  1. Start Small: Focus on high-impact areas first (e.g., customer support, product specs).
  2. Iterate: Test knowledge entries in specific tasks, refine triggers/content based on AI performance.
  3. Version Control: Update entries regularly (e.g., "Q3 Sales Playbook" → "Q4 Sales Playbook").
  4. Collaborate: Share knowledge libraries across teams (sales, support, dev) for consistency.
  5. Security: Avoid storing sensitive data (passwords, PII) unless encrypted.

How to Use Knowledge for Maximum Benefit

1. Centralize Critical Information

- Example:

  • Name: "Company Brand Guidelines"
  • When to Use: "Use when generating marketing copy or social media posts."
  • Content: Include tone-of-voice rules, approved slogans, and brand color codes.

- Why: Ensures consistency in outputs (e.g., avoiding off-brand language).

2. Define Clear Triggers

- Example:

  • Name: "Technical Support Scripts"
  • When to Use: "Use when resolving user queries about software errors."
  • Content: Troubleshooting steps, error code explanations, escalation protocols.

- Why: Prevents the AI from misapplying knowledge in unrelated contexts.

3. Structure Content for AI Readability

- Good: Structured data (bullet points, headers) helps the AI parse and apply information accurately.

- Bad: Vague, unstructured content (e.g., "Refund Policy: Customers can get refunds sometimes, depending on the situation.")

- Why: Structured data helps the AI parse and apply information accurately.

4. Use for Industry-Specific Context

- Example:

  • Name: "Healthcare Compliance Rules"
  • When to Use: "Use when drafting patient communication or handling PHI data."
  • Content: HIPAA guidelines, patient consent templates, redaction protocols.

- Why: Ensures compliance and reduces legal risks.

5. Enable/Disable Knowledge Dynamically

- Example: Disable "Holiday Promotions" knowledge outside Q4 to avoid outdated marketing suggestions.

- Why: Maintains relevance and prevents conflicting information.

Common Use Cases

Scenario Knowledge Entry Outcome
Customer Service FAQ Database + Escalation Workflows Faster, accurate ticket resolution.
Software Development API Documentation + Code Standards Code generation aligns with internal norms.
Marketing Campaigns Brand Voice + Campaign Timelines On-brand, deadline-aware content creation.
Legal/Compliance Regulatory Guidelines Compliant contract drafting and redaction.

Pitfalls to Avoid

  • Overloading: Too much content can confuse the AI. Keep entries concise.
  • Vague Triggers: Ambiguous "When to Use" conditions lead to irrelevant references.
  • Stale Data: Failing to update entries (e.g., outdated pricing) causes errors.