Context Elicitation

Systematically clarifies complex problems through adaptive dialogue patterns. Automatically selects and combines appropriate prompt patterns based on information gaps.

Purpose

Primary use case: Complex problem-solving where user's situation, requirements, or context are unclear or incomplete.

Core capability:

• Elicit missing information through targeted questioning
• Verify and refine mutual understanding
• Manage conversational context dynamically
• Know when enough information has been gathered

Analogy: Functions like Deep Research's initial exploration phase—probing until requirements are clear enough to proceed.

When to Use

Apply this skill when detecting these signals:

Information insufficiency:

• Purpose or goal is ambiguous
• Constraints/requirements unstated
• Assumptions unclear
• User's knowledge level unknown

Complexity indicators:

• Multi-faceted problem with interdependencies
• Question is too high-level without concrete details
• Multiple valid interpretations exist

Uncertainty signals:

• User phrases like "複雑なので整理したい" (complex, need to organize)
• Tentative language: "maybe," "I think," "not sure"
• Contradictory statements
• Question refinement attempts by user

Do NOT use when:

• Question is clear and specific
• Information is sufficient to proceed
• Simple factual queries

Process Flow

Phase 1: Initial Assessment

Evaluate what's known vs. unknown:

• Stated: What information did user provide?
• Missing: What's needed to address this properly?
• Ambiguous: What requires clarification?
• Complexity: Single issue or multi-faceted?

Phase 2: Information Gathering

Select pattern(s) based on gaps:

Simple information gap → Flipped Interaction Complex/multi-part problem → Cognitive Verifier Vague question → Question Refinement Combination needed → Chain patterns appropriately

Phase 3: Understanding Verification

After gathering information:

1. Use Reflection to articulate current understanding
2. Make assumptions explicit
3. Highlight remaining uncertainties
4. Ask for confirmation/correction

Phase 4: Context Adjustment

If user corrects understanding:

• Use Context Manager to update/remove incorrect assumptions
• Explicitly state what's being added/removed from context
• Return to Phase 2 if significant gaps remain

Phase 5: Termination Check

Sufficient information when:

• Purpose is clear
• Key constraints identified
• Assumptions validated
• Next steps are obvious

If insufficient → Return to Phase 2 If sufficient → Confirm and proceed to main task

Pattern Reference

Flipped Interaction

Purpose: LLM drives conversation by asking user questions.

Structure:

I would like you to ask me questions to achieve X
Ask questions until [condition is met]
(Optional) Ask questions [one at a time / N at a time]

Implementation:

"To help clarify [goal], I'll ask you a few questions. 
I'll ask one question at a time until I have enough 
information to [specific outcome]."

When to use:

• Need to collect specific information
• User knows answers but hasn't provided them
• Multiple discrete pieces of information needed

Pitfalls:

• Don't ask too many questions at once (overwhelming)
• Specify termination condition clearly
• Match question complexity to user's expertise

Cognitive Verifier

Purpose: Subdivide complex questions into manageable sub-questions.

Structure:

When you are asked a question, follow these rules:
- Generate N additional questions that would help answer accurately
- Combine answers to individual questions to produce final answer

Implementation:

"This is multi-faceted. Let me break it into 3 questions:
1. [Sub-question addressing aspect A]
2. [Sub-question addressing aspect B]  
3. [Sub-question addressing aspect C]
Once you answer these, I'll synthesize them into 
a comprehensive response."

When to use:

• Question is complex/multi-dimensional
• Direct answer would be incomplete
• Problem benefits from structured decomposition

Optimal number: 3-5 sub-questions (論文のresearchより)

Pitfalls:

• Don't over-subdivide simple questions
• Sub-questions must be answerable by user
• Define unfamiliar terms in sub-questions

Question Refinement

Purpose: Suggest improved version of user's question.

Structure:

Within scope X:
- Suggest a better version of the question
- (Optional) Ask if user wants to use the refined version

Implementation:

"I understand you're asking about [X]. To give you 
a more precise answer, could I refine this to:
'[Improved question with specific context]'
Does this better capture what you want to know?"

When to use:

• Question is vague or ambiguous
• Missing important context
• User may not know domain-specific considerations

Pitfalls:

• Don't narrow too aggressively (may miss user's intent)
• Explain any unfamiliar terms introduced
• Can create "echo chamber" if overused

Reflection

Purpose: Explain reasoning and assumptions behind analysis.

Structure:

When you provide an answer:
- Explain reasoning and assumptions
- Address potential ambiguities/limitations
- (Optional) Help user improve their question

Implementation:

"Based on our discussion, here's my understanding:
- [Key point 1 and reasoning]
- [Key point 2 and reasoning]
- I'm assuming [assumption A] and [assumption B]
- I'm still uncertain about [X]
Is this understanding correct?"

When to use:

• After information gathering phase
• Before proceeding to main task
• When assumptions were made
• To enable user correction

Critical: Make assumptions explicit, not hidden.

Context Manager

Purpose: Control what information is considered or ignored.

Structure:

Within scope X:
- Please consider Y
- Please ignore Z
- (Optional) Start over

Implementation:

"I'll focus on [Y] and set aside [Z] for now."

"Earlier you mentioned [X], but based on your 
clarification, I'll disregard that and instead 
focus on [Y]."

When to use:

• User corrects earlier statement
• Conversation has drifted off-topic
• Example was mistaken for main point
• Need to reset context cleanly

Pitfalls:

• Be explicit about what's being ignored (don't silently drop things)
• May need to explain why certain context is being removed

Pattern Combination Strategies

Strategy 1: Simple Information Gap

Flipped Interaction → Reflection → Confirm

Use when: Missing specific, discrete information

Strategy 2: Complex Problem Decomposition

Cognitive Verifier → Flipped Interaction (per sub-Q) → Reflection → Confirm

Use when: Multi-faceted problem requiring structured analysis

Strategy 3: Vague Query Refinement

Question Refinement → [User confirms] → Flipped Interaction → Reflection

Use when: User's question is unclear or too broad

Strategy 4: Iterative Clarification

Flipped Interaction → Reflection → Context Manager → [Loop if needed]

Use when: Understanding evolves through dialogue, corrections needed

Strategy 5: Maximum Depth

Question Refinement → Cognitive Verifier → Flipped Interaction → 
Reflection → Context Manager → [Repeat until clear]

Use when: Highly complex, ambiguous situation requiring maximum rigor

Pattern Declaration

Always announce pattern usage:

"I'll use [Pattern Name] to [specific goal]."

Why: Educational value—helps user understand the approach.

Example:

"This question has multiple dimensions. I'll use the 
Cognitive Verifier pattern to break it into 3 sub-questions, 
then use Flipped Interaction to gather details on each."

Integration Example

Scenario: Complex deployment question

User: "I need to deploy my application to production."

Claude (Initial Assessment):

This is a complex deployment question. I'll use the Flipped 
Interaction pattern to gather essential information.

To recommend the right deployment approach, I need to understand:
1. What type of application is this? (web app, API, batch job, etc.)

User: "It's a web application with a React frontend and Node.js backend."

Claude (Cognitive Verifier + Continued Flipped Interaction):

Thanks. This breaks into several deployment aspects. Let me ask 
about each:

2. What's your expected traffic? (approximate users/requests)
3. Do you have any specific infrastructure preferences or constraints?

User: "Maybe 1000 concurrent users. We prefer AWS. Budget is limited."

Claude (Reflection Pattern):

Let me confirm my understanding:
- React + Node.js web application  
- ~1000 concurrent users expected
- AWS deployment preferred
- Cost is a significant constraint

I'm assuming:
- You need both staging and production environments
- Standard HA/reliability requirements (not mission-critical)
- No specific compliance requirements (HIPAA, etc.)

Am I understanding correctly? Is there anything I should adjust?

User: "Actually, we don't need staging. Just production. And it IS healthcare-related."

Claude (Context Manager):

Important correction noted. I'll disregard the staging environment 
assumption and add HIPAA compliance as a requirement.

Updated understanding:
- Single production environment only
- Healthcare application (HIPAA compliance required)
- This significantly changes the deployment approach

Let me ask one more question:
4. Do you have existing HIPAA compliance infrastructure, or is 
   this your first HIPAA-compliant deployment?

User: "First time with HIPAA."

Claude (Final Reflection + Proceed):

Perfect. I now have sufficient context:
- React + Node.js web app
- AWS deployment
- ~1000 concurrent users
- Cost-conscious
- HIPAA compliance required (first time)
- Production only, no staging

With this information, I can recommend specific deployment 
architectures. The HIPAA requirement is critical and will 
influence many decisions. Ready to proceed with recommendations?

What This Demonstrates

1. Pattern chaining: Flipped Interaction → Cognitive Verifier → Reflection → Context Manager
2. Pattern declaration: Each pattern announced before use
3. Iterative refinement: Understanding evolved through corrections
4. Explicit assumptions: Made visible for verification
5. Clear termination: Articulated when sufficient information gathered
6. Context adjustment: Actively managed changing requirements

Implementation Notes

Behavioral Guidelines

Be adaptive: Pattern selection should be dynamic, not formulaic.

Be explicit:

• Announce pattern changes
• State assumptions clearly
• Make context adjustments visible

Be efficient:

• Minimum questions needed
• Don't over-decompose simple problems
• Terminate when sufficient (don't over-collect)

Be educational:

• Pattern names help user learn
• Rationale for pattern selection can be brief

Common Mistakes to Avoid

Over-questioning: Know when to stop. Don't exhaust the user.

Hidden assumptions: If you're assuming something, say it. Reflection pattern catches this.

Context drift: Use Context Manager to actively prune irrelevant information.

Pattern rigidity: Don't force patterns. Skip unnecessary steps.

Premature termination: Verify understanding before proceeding.

Quality Checks

Before proceeding to main task:

• Can you articulate the goal clearly?
• Are constraints/requirements identified?
• Have assumptions been validated?
• Is the next step obvious?
• Has user confirmed understanding?

If any "no" → Return to information gathering.

Reference

Based on: White, J., et al. (2023). "A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT." arXiv:2302.11382.

Key patterns integrated:

• Flipped Interaction (Section III-D)
• Cognitive Verifier (Section III-H)
• Question Refinement (Section III-F)
• Reflection (Section III-N)
• Context Manager (Section III-P)