SocraticQuestioner

Category: Specialized | Module: mycontext.templates.free.specialized

Applies the classical Socratic method to examine statements, claims, and positions. Generates 2–3 penetrating questions across six categories: clarification, assumptions, evidence, alternative perspectives, implications, and meta-questions. Reveals what's taken for granted and what needs examination.

When to Use

• Strategic decision examination before committing
• Evaluating business proposals or plans
• Critical thinking training
• Examining AI-generated conclusions
• Reviewing arguments or recommendations
• Philosophy, ethics, or policy analysis
• Challenging your own team's assumptions

Quick Start

from mycontext.templates.free.specialized import SocraticQuestioner

questioner = SocraticQuestioner()

ctx = questioner.build_context(
    statement="We should implement AI in our hiring process",
    depth="thorough",
)
result = ctx.execute(provider="openai")
print(result.response)

Methods

build_context(statement, context="", depth="detailed")

Parameters:

Parameter	Type	Default	Description
statement	str	required	The claim, belief, or position to examine
context	str	""	Background context for the statement
depth	str	"detailed"	Depth of inquiry

execute(provider, statement, context="", depth="detailed", **kwargs)

result = questioner.execute(
    provider="openai",
    statement="Remote work is more productive than office work",
    depth="thorough",
)

Six Categories of Socratic Questions

1. Clarifying Questions

Ensure the statement is precisely understood before examining it.

• What exactly do you mean by "implement AI"?
• What specific part of hiring — screening, interviewing, selection?
• Does "we" mean all roles or specific ones?

2. Probing Assumptions

Surface what's taken for granted without evidence.

• What are you assuming about AI's ability to judge candidates?
• Are you assuming current hiring is unbiased?
• What would change if those assumptions were wrong?

3. Probing Reasons and Evidence

Test the factual foundation of the statement.

• What evidence shows AI improves hiring outcomes?
• What data do you have about current hiring quality?
• What would change your view on this?

4. Questioning Viewpoints

Surface alternative perspectives.

• How would a rejected candidate view this?
• What would an employment lawyer say?
• What does research on algorithmic bias show?

5. Probing Implications

Follow the claim to its consequences.

• If implemented, what legal obligations arise?
• If it works, what happens to human hiring expertise?
• What precedent does this set for other AI decisions?

6. Questioning the Question

Examine whether this is even the right question.

• Why is this the proposed solution? What problem are we solving?
• Is "implement AI" the right level of specificity?
• What better question should we be asking?

Depth Levels

Depth	Questions per category	Use case
"basic"	1	Quick assumption check
"detailed"	2	Standard Socratic examination
"thorough"	3	Deep, comprehensive inquiry

Examples

Strategic Decision

result = questioner.execute(
    provider="openai",
    statement="We should pivot from B2B to B2C to access a larger market",
    depth="thorough",
)

Technical Claim

result = questioner.execute(
    provider="anthropic",
    statement="Microservices are always better than monoliths for scalability",
    depth="detailed",
)

Policy Examination

result = questioner.execute(
    provider="gemini",
    statement="Mandatory office attendance 3 days per week will improve team collaboration",
    context="Post-pandemic policy proposal for a 200-person technology company",
    depth="thorough",
)

AI Output Verification

# Use Socratic questioning to critically examine LLM outputs
llm_conclusion = "Your product should target enterprise customers because they have higher LTV"

result = questioner.execute(
    provider="openai",
    statement=llm_conclusion,
    context="Early-stage startup, $0 revenue, B2C product currently",
    depth="detailed",
)

The Synthesis Section

After the six categories, the pattern synthesizes the most important insights the questions collectively reveal — what they expose about the statement's strengths, weaknesses, and blind spots.

Chaining with Other Patterns

Combine with RiskAssessor for complete decision examination:

from mycontext.templates.free.specialized import SocraticQuestioner, RiskAssessor

statement = "We should acquire a competitor to accelerate growth"

# Step 1: Examine assumptions
soc_result = SocraticQuestioner().execute(
    provider="openai",
    statement=statement,
    depth="thorough",
)

# Step 2: Assess risks of proceeding
risk_result = RiskAssessor().execute(
    provider="openai",
    decision=statement,
    context=soc_result.response[:500],
    depth="comprehensive",
)

API Reference

Method	Returns	Description
build_context(statement, context, depth)	Context	Assembled context
execute(provider, statement, context, depth, **kwargs)	ProviderResponse	Execute questioning
generic_prompt(statement, context_section, depth)	str	Zero-cost prompt string