How to Configure AI to Remember My Research Needs?

Question Type

User Manual Question

Quick Answer

Memory System works automatically, no manual setup required.

AI Automatically Extracts and Remembers:

✅ User profile (industry, role, company)
✅ Research preferences (preferred methods and style)
✅ Research history (past research and key findings)
✅ Brand positioning (your unique positioning and differentiation)

Just have normal conversations, AI automatically learns and remembers.

How Does Memory System Work?

Automatic Extraction: No Manual Input Required

You Don't Need To:

❌ Fill out "profile" forms
❌ Manually set "research preferences"
❌ Proactively tell AI "remember this"

AI Automatically Extracts from Conversations:

1st Conversation:

You: "I want to understand new trends in the healthy snack market"AI: "Okay, let me first understand the background:    - What industry are you in?    - Who is your target audience?    - Which market segment do you focus on?"You: "I'm a brand manager at a consumer goods company,    responsible for the healthy snack product line.    Target audience is 25-35 year old women."→ AI automatically extracts and remembers:  - Industry: Consumer goods / Healthy snacks  - Role: Brand manager  - Target audience: 25-35 year old women

5th Conversation (AI already remembers):

You: "I want to create an emotional snack product"AI: (No need to repeat background)"Based on your previous focus on healthy snacks, I've noticed emotional snacks are an emerging trend. Your target audience (25-35 year old women) has strong demand for 'healing'..."→ AI recalls memory:  - Industry: Healthy snacks  - Target audience: 25-35 year old women  - Research history: Market trend analysis

Value:

Save time: No need to repeat background each time
Natural conversation: No special format or commands needed
Zero burden: Normal conversation is enough

Automatic Organization: Keep Memory Concise

Problem: As usage time increases, memory accumulates

Solution: AI automatically organizes

Organization Mechanism:

When memory exceeds threshold (e.g., 100 entries):1. Deduplicate: Delete duplicate information   - "Target audience 25-35 year old women" mentioned 10 times → Keep only 12. Compress: Merge similar memories   - 5 research projects all mention "emotional value" → Compress to "Core focus: emotional value"3. Prioritize: Keep most important, delete outdated   - Temporary decisions from 6 months ago → Delete   - Core brand positioning → Keep4. Version management: Traceable history   - Retain complete change history

Example:

Before Organization (100 entries):- Memory 1: Target audience is 25-35 year old women- Memory 5: We focus on 25-35 year old urban women- Memory 12: Main demographic is 25-35 year old working women- Memory 18: Core users are still 25-35 year old women(Repeated 4 times)After Auto-Organization (30 core memories):- Core target demographic: 25-35 year old urban working women (confirmed across 4 research projects)

Tips to Accelerate Memory Building

Although AI learns automatically, you can accelerate this process:

Tip 1: Provide More Background Information Initially

First 5 conversations, proactively introduce yourself:

Good Example:

"I'm a brand manager at a consumer goods company, responsible for the healthy snack product line. Our target audience is 25-35 year old urban women who care about health but don't want to be extreme. I prefer to quickly validate direction first, then research deeply."→ AI extracts 6 key pieces of information:  1. Role: Brand manager  2. Industry: Consumer goods / Healthy snacks  3. Target audience: 25-35 year old urban women  4. User characteristics: Health-conscious but not extreme  5. Research preference: Quick validation priority  6. Company type: Consumer goods company

Bad Example:

"I want to do market research"→ AI only knows: User wants market research→ Requires multiple conversations to build complete understanding

Tip 2: Clearly State Your Brand Positioning

Brand positioning is core of Memory:

Example:

You: "Our brand positioning is 'delicious healthy snacks + emotional healing',    not functional health snacks, and not extreme fitness snacks.    We want users to feel 'eating healthy without guilt'."→ AI remembers:  - Brand positioning: Delicious health + emotional healing  - Core difference: Emotional value, not functional  - Target psychology: Eating healthy without guilt

Value:

AI's subsequent recommendations based on this positioning
Avoid AI giving recommendations that don't match positioning

Tip 3: State Your Research Objectives

When starting each research, state objectives:

Example:

You: "I want to test new packaging design,    hope packaging reflects our brand positioning (emotional healing),    while attracting 25-35 year old women to purchase."→ AI remembers:  - Current research: Packaging design testing  - Core objective: Reflect brand positioning + attract target audience

Value:

AI's research plan more precise
AI's recommendations better match your objectives

What Does Memory Remember?

1. User Profile

Automatically extracts your basic information:

Memory Content:

- Industry: Consumer goods, tech, finance, education...- Role: Product manager, brand manager, entrepreneur, researcher...- Company size: Startup, medium enterprise, large enterprise...- Focus areas: Healthy food, consumer electronics, SaaS...

Example:

1st conversation: "I'm a brand manager at a consumer goods company,responsible for healthy snack product line"Memory extracts:- Industry: Consumer goods / Healthy snacks- Role: Brand manager- Product line: Healthy snacks- Company type: Consumer goods company10th conversation:AI: "As a healthy snack brand manager, you might be concerned..."(No need to repeat introduction)

2. Research Preferences

Remembers your preferred working style:

Memory Content:

- Research method preference: Like Discussion for quick validation or Interview for deep diving?- Report style: Like detailed data or concise insights?- Persona preference: Trust high-quality custom personas or public persona library is okay?- Pace preference: Quick output or deep research?

Example:

3rd research: User repeatedly chooses "Discussion first then Interview" workflowMemory learns:- User preference: Quick validation → Deep diving10th research:AI: "Based on your research habits, I suggest first using Discussion to    quickly test 3 directions, then deeply interview the most promising direction"

3. Research History

Complete record of your research trajectory:

Memory Content:

- Past projects: What research have you done?- Key findings: What were the key insights from each research?- Research direction: From market analysis to product validation to packaging testing...- Timeline: Complete research history arranged chronologically

Example:

User's research history (Memory auto-records):2026-01: Healthy snack market trend analysis- Key finding: Emotional value becoming new selling point2026-02: Emotional snack product concept validation- Key finding: Users willing to pay premium for "healing feeling"2026-03: Packaging design testing- Key finding: Refined + emotional label design most popular2026-04 (now): Pricing strategy research- AI: "Based on your previous findings (emotional value, healing feeling, refined packaging),       I suggest pricing at ¥8-12, emphasizing emotional experience rather than health function..."

Value:

AI understands your complete research context
New research can build on old findings, not starting from scratch
Forms knowledge accumulation, not fragmented research

4. Recurring Themes

Identifies topics you consistently focus on:

Memory Content:

- If you repeatedly research "young female consumers", AI knows this is your core demographic- If you repeatedly mention "emotional value", AI understands this is your brand positioning- If you often ask about "25-35 year old users", AI remembers this is your target age range

Example:

User mentions "25-35 year old urban women" in 5 research projectsMemory identifies:- Core target demographic: 25-35 year old urban women6th research:AI: (Automatically uses 25-35 year old urban women as default demographic, no need to ask again)"I will conduct research based on your core target demographic (25-35 year old urban women)..."

5. Brand and Product Positioning

Understands your core positioning:

Memory Content:

- Brand tone: Healthy, healing, energetic, premium...- Product features: Functional, emotional value, value for money...- Differentiation strategy: What's the core difference from competitors?

Example:

First 10 research: User repeatedly emphasizes "delicious healthy snacks", "not too fitness-oriented", "need emotional value"Memory extracts:- Brand positioning: Healthy but not extreme + emotional value- Target demographic: Health-conscious but not fitness fanatics- Core difference: Emotional healing, not functional health15th research:AI: "Based on your brand positioning (delicious health + emotional healing),    I suggest product packaging emphasize 'eat happily without guilt',    rather than 'low-calorie high-protein'..."

View and Manage Memory

View Current Memory

Entry:

Account Settings → "Memory" tab
Or directly visit /settings/memory

View Content:

Core Memory:- User profile- Brand positioning- Core target demographic- Research preferencesWorking Memory:- Recent research history- Temporary decisions- Current focus points

Manual Memory Editing (Optional)

If AI understands incorrectly, can manually correct:

Steps:

Go to Memory management page
Click "Edit Core Memory"
Modify or supplement content
Save

Example:

AI remembers:- Target audience: 25-35 year old womenYou want to correct to:- Target audience: 25-40 year old women (expand age range)After manual editing:- AI's subsequent recommendations based on 25-40 year old women

Recommendation:

Generally no need for manual editing
Only correct when AI clearly misunderstands
Or update when strategic direction major adjustment

Delete Memory (Optional)

Scenarios:

Brand strategy adjustment: Delete old positioning memory
Changed target market: Delete old market memory
Privacy considerations: Delete sensitive information

Steps:

Go to Memory management page
Select memory to delete
Click "Delete"
Confirm deletion

Batch Clear:

Delete all memories from a time period
Delete all memories on a topic
One-click clear all memories (start fresh)

Team Sharing: Team-Level Memory

Personal vs Team Version

Personal Version:

Only your own memory
Suitable for individual users, independent researchers

Team Version:

Entire team shares same Memory
All members' research accumulates in team memory
New members join without repeating introductions

Value of Team Memory

Scenario 1: New Employee Onboarding

Traditional Way:

New hire: "What's our positioning? Who's our target audience?"Requires senior employee time to explainNew hire still doesn't fully understand

Memory System (Team Version):

New hire directly asks AI: "What's our brand positioning?"AI: "Based on team's past 6 months of 30 research projects, your brand positioning is:    - Delicious healthy snacks + emotional healing value    - Target audience: 25-35 year old urban women    - Core difference: Emotional healing, not functional health    - Pricing strategy: Mid-to-high end (¥8-12)    - Key finding: Users willing to pay premium for emotional value"New hire understands complete background in 5 minutes, no senior employee explanation needed

Scenario 2: Cross-Department Collaboration

Traditional Way:

Marketing did user researchProduct department doesn't know research resultsRepeat research, waste time

Memory System (Team Version):

Marketing completes research → Automatically recorded in team MemoryProduct department asks AI: "What's users' true attitude toward our product?"AI calls up marketing's research results, answers directlyAvoid duplicate research

Scenario 3: Long-Term Knowledge Accumulation

Traditional Way:

Research reports scattered everywhereForget previous findings after a few monthsNew research cannot build on old findings

Memory System (Team Version):

All research automatically accumulates in team MemoryAI understands team's complete research historyNew research can build on old findings, forming knowledge network

Team Memory Privacy Protection

Isolation Mechanism:

Each team's Memory completely isolated
Team A cannot see Team B's memory
Personal users cannot see team users' memory

Permission Management:

Team administrators can control which members access Memory
Can set read-only permissions (view but cannot modify)
Can revoke access when members leave

FAQ

Q1: Does Memory Affect AI's Objectivity?

No, Memory only provides context, doesn't change judgment logic:

Case 1:

User memory: "We think product A is great"New research finds: Users don't like product AAI response:"Although you previously thought product A was good, according to this research, user feedback shows product A has following issues..."→ AI won't change objective judgment because of Memory

Case 2:

User memory: "Target audience is 25-35 year old women"New research scope: 40-50 year old usersAI response:"Note: This research subjects (40-50 years old) differ from your core target demographic (25-35 years old). Research results may not directly apply to core demographic..."→ Memory helps AI provide more accurate contextual reminders

Q2: Won't Too Much Memory Confuse AI?

No, has auto-organization mechanism:

Trigger Conditions:

Memory exceeds 100 entries
More than 30 days since last organization
User can manually organize anytime

Organization Content:

Deduplicate: Delete duplicate memories
Merge: Combine similar memories
Compress: Extract core information, delete redundant details
Archive: Archive outdated but potentially useful memories

Effect:

Memory stays concise and efficient
Key information not buried
AI response speed unaffected

Q3: Can Memory Be Exported?

Yes, supports export:

Export Formats:

Markdown file (suitable for reading)
JSON file (suitable for program processing)
PDF report (suitable for sharing)

Export Content:

Complete memory list
Organized by category (user profile, research preferences, research history...)
Includes version history

Use Cases:

Report to boss: Export research history
Team meeting: Export team consensus
Backup: Regularly export memory backup

Q4: If Change Jobs, What About Memory?

Personal memory vs team memory handled separately:

Personal Memory:

Follows your account
Retained after changing jobs
Can choose to delete (if don't want to keep old company info)

Team Memory:

Belongs to team, not individual
Automatically lose access after leaving
Won't take away team memory

Privacy Protection:

When leaving, can choose "clear personal contributions to memory"
Only delete content you personally added, doesn't affect others

Q5: How Often Does Auto-Organization Trigger?

Dynamic trigger mechanism:

Trigger Conditions:

Quantity threshold: Memory exceeds 100 entries
Time threshold: More than 30 days since last organization
Manual trigger: User can manually organize anytime

Organization Process:

Before organization: AI reminds"Your memory about to be organized, expect to compress 30% content"After organization: AI summarizes"Organization complete, compressed from 100 to 70 core memories"

Best Practices

Recommendation 1: Provide More Background Initially

First 5 conversations, proactively introduce:

Industry, role, objectives
Brand positioning and differentiation
Research preferences

Value:

Help Memory quickly build understanding
AI recommendations more precise

Recommendation 2: Regularly Review Memory for Accuracy

Recommended frequency: Review once per month

Check Content:

Is information AI remembers accurate?
Any outdated information needing deletion?
Any new core information needing emphasis?

Value:

Ensure AI understanding is accurate
Promptly correct deviations
Improve recommendation quality

Recommendation 3: Team Version Regular Memory Alignment

Problem: Team members may have inconsistent understanding of certain concepts

Recommendation: Hold "memory alignment meeting" each quarter

Process:

Export team Memory
Team reviews together
Discuss inconsistencies
Unify understanding and update Memory

Value:

Ensure team understanding consistent
Memory becomes carrier of "team consensus"

Recommendation 4: Don't Over-Rely on Memory

Memory's Role:

✅ Provide context, save time
✅ Personalized recommendations
❌ Doesn't replace independent thinking

Recommendation:

For each important decision, re-validate assumptions
Don't skip validation just because "Memory says so"
Market changes, Memory can become outdated

Example:

Memory: "Target audience is 25-35 year old women"6 months later, market changes, Gen Z becomes new opportunityCorrect approach:- Use Scout to re-observe market- Validate if Gen Z is really opportunity- Update Memory: "Expand to Gen Z"Wrong approach:- Don't look at Gen Z just because Memory says "25-35 years old"

Final Takeaway

"Memory System works automatically, no manual setup required. Just have normal conversations, AI automatically extracts, organizes, remembers. The more you use, the better it understands you, recommendations become increasingly precise."

Remember:

✅ Automatic extraction: Normal conversation, AI learns automatically
✅ Automatic organization: Memory stays concise, won't get messy
✅ Accelerate building: Provide more background initially
✅ Can view and manage: Account Settings → Memory
✅ Team sharing: Team-level Memory, knowledge transfer

Related Feature: Memory System Document Version: v2.1 Update Date: 2026-02-02 Update Notes: Updated terminology and platform information