Socratic AI Coding: A New Programming Collaboration Paradigm ​

"I know that I know nothing" — Socrates

Abstract ​

This article proposes a new AI-assisted programming paradigm: Socratic AI Coding. Unlike the traditional "command-execute" model, this method uses vague but guiding questions to stimulate AI's deep reasoning capabilities, thus producing higher quality code.

Empirical observations show that: vague prompts often produce better code than explicit instructions. This article systematically explains this phenomenon and distills a practical methodology.

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Table of Contents ​

1. Theoretical Foundation: From Philosophy to Programming
2. Core Concepts and Terminology
3. Deep Comparison of Two Paradigms
4. Five Core Mechanisms
5. Practice Principles
6. Case Studies
7. Deep Insights
8. Best Practices Guide

---

Theoretical Foundation: From Philosophy to Programming ​

The Essence of the Socratic Method ​

In the 5th century BC, Socrates invented a unique teaching method: not giving answers directly, but guiding students to discover truth themselves through questioning.

Core Principle:

• ❌ Don't say: "The answer is X"
• ✅ Instead ask: "What if it's Y? What about Z?"
• 🎯 Goal: Stimulate critical thinking, make students actively reason rather than passively accept

Migration to AI Coding ​

Traditional AI programming collaboration model:

Human: Add if (x > 0) check at line 42
AI:    OK, added

This is the command-execute mode, AI is a tool.

Socratic AI Coding:

Human: There's an edge case not handled here, what should we do?
AI:    Let me first understand the code logic...
       Found the problem: x could be negative
       Referenced existing patterns: other places all use validate_input
       Suggested solution: add unified input validation

This is the problem-explore-design mode, AI is a collaborator.

Key Differences ​

Dimension	Traditional Method	Socratic Method
Human Role	Designer	Questioner
AI Role	Executor	Thinker
Knowledge Transfer	Explicit telling	Guided reconstruction
Output Quality	Locally correct	Globally optimal

---

Core Concepts and Terminology ​

1. Socratic Prompting ​

Definition : Vague but guiding prompts that leave exploratory space for AI.

Examples:

 ✅ "HTTP request failed, how should curl be logged?"
✅ "This design might not be good, is there a better solution?"
✅ "Logs show there's a problem here, but I'm not sure why"

2. Instructive Prompting ​

Definition : Clear specific commands, AI just needs to execute.

Examples:

 "Add try-except at line 42"
"Change variable name from foo to bar"
"Delete this function"

3. Pattern Discovery ​

Definition : AI actively identifies and aligns with architectural patterns from the codebase.

Example:

python

# AI discovers existing pattern:
FunctionCallStartEvent    → Start
FunctionCallCompleteEvent → Success
FunctionCallErrorEvent    → Failure ✅

# Infers missing pattern:
StreamStartEvent    → Start
StreamCompleteEvent → Success
StreamErrorEvent    → Failure ❌ (needs to be added)

4. Forced Understanding ​

Definition : Vague prompts force AI to deeply understand the codebase, not just make surface modifications.

Mechanism:

1. Receive vague question
2. Cannot execute directly
3. Must first understand context
4. Reason out solution
5. Verify solution feasibility

5. Trust Inversion ​

Definition : Shift from "Human designs → AI executes" to "AI designs → Human reviews".

Effect:

• AI takes on design cognitive load
• Human takes on review cognitive load
• AI's design capability is fully activated

---

Deep Comparison of Two Paradigms ​

Execution Flow Comparison ​

Instructive AI Coding ​

User: Add Y at position X
  ↓
AI: Locate file
  ↓
AI: Find position X
  ↓
AI: Insert code Y
  ↓
Output: Code modified

Characteristics:

• ✅ Fast, precise
• ❌ Limited vision
• ❌ Doesn't understand "why"
• ❌ May execute wrong solution

Socratic AI Coding ​

User: X has a problem, what should we do?
  ↓
AI: Understand problem domain
  ↓
AI: Search related code
  ↓
AI: Identify existing patterns
  ↓
AI: Design solution
  ↓
AI: Complete implementation (multiple files)
  ↓
AI: Verification (type checking, tests)
  ↓
Output: Complete solution with architectural thinking

Characteristics:

• ✅ Deep understanding
• ✅ Architectural alignment
• ✅ End-to-end complete
• ⚠️ Requires more reasoning time

Quality Dimension Comparison ​

Dimension	Instructive	Socratic
Syntactic Correctness	✅ High	✅ High
Architectural Consistency	⚠️ Low	✅ High
Completeness	⚠️ Partial	✅ End-to-end
Maintainability	⚠️ Low	✅ High
Test Coverage	❌ None	✅ Includes verification
Pattern Alignment	❌ Not considered	✅ Actively aligned

Code Quality Comparison (Real Case) ​

Scenario : Need to log curl command when HTTP request fails for debugging

Instructive Solution ​

python

# User: "Add yield StreamErrorEvent in execute()"

async def execute(self):
    stream = await self.client.responses.create(...)

    yield StreamStartEvent(...)

    async for event in stream:
        yield event

    # ❌ Problem 1: No try-except
    # ❌ Problem 2: StreamStartEvent won't be yielded on failure
    # ❌ Problem 3: curl command lost
    # ❌ Problem 4: StreamErrorEvent definition not added
    # ❌ Problem 5: No formatting logic added

Socratic Solution ​

python

# User: "HTTP failed, how should curl be logged?"

# AI reasoning process:
# 1. Need curl when HTTP fails → must get it before failure
# 2. Check existing event system → found FunctionCallErrorEvent
# 3. Identify pattern: Start/Complete/Error three states
# 4. Design StreamErrorEvent aligned with existing pattern
# 5. yield + raise ensures event logging and exception propagation

# ✅ Complete solution:

# 1. Define event type
class StreamErrorEvent(LLMEvent):
    model: str
    provider: str
    error_type: str
    error_message: str
    curl_command: str | None

# 2. Modify handler
async def execute(self):
    curl_command = self.http_debug.last_curl_command

    # Yield StreamStartEvent first (ensure curl is logged)
    yield StreamStartEvent(curl=curl_command)

    try:
        stream = await self.client.responses.create(...)
        async for event in stream:
            yield event
    except Exception as e:
        # Yield error event (includes curl)
        yield StreamErrorEvent(
            error_message=str(e),
            curl_command=curl_command,
        )
        raise  # Continue raising exception

# 3. Add formatting logic
def _format_stream_error(self, event):
    return f"""
❌ Request failed: {event.error_type}
Error details: {event.error_message}
cURL Command (can be copied and executed):
{event.curl_command}
"""

# 4. Write test verification
async def test_stream_error_event():
    events = []
    try:
        async for event in simulate_failed_request():
            events.append(event)
    except Exception:
        pass

    assert len(events) == 2
    assert isinstance(events[0], StreamStartEvent)
    assert isinstance(events[1], StreamErrorEvent)

Result Comparison:

• Instructive: 1 file modified, 5 problems
• Socratic: 4 files modified, complete solution, test verification

---

Five Core Mechanisms ​

1. Constraint Avoidance ​

Problem : Explicit instructions may be based on wrong assumptions.

Case:

python

# ❌ Wrong instruction: "Add error field to StreamStartEvent"
class StreamStartEvent:
    model: str
    error: str | None  # Violates single responsibility!

# ✅ Socratic: "HTTP failed, how to log it?"
# AI reasoning → discovers should create separate StreamErrorEvent

Principle : Vague prompts give AI space to discover better solutions.

2. Forced Understanding ​

Mechanism : Vague prompts cannot be executed directly, must first understand the codebase.

Comparison:

Instructive	Socratic
"Change line 42"	"There's a bug here, how to fix?"
→ Locate line	→ Understand code logic
→ Modify code	→ Identify root cause
→ Done	→ Find similar patterns
→ Design complete solution
→ Coordinated multi-file changes
→ Verification and testing

Result : Deep understanding produces high quality code.

3. Trust Inversion ​

Traditional Mode:

 Human: I've thought it through, just do it
AI:    OK (doesn't dare question even if finds problems)

Socratic Mode:

 Human: There's a problem, can you help me think? Maybe do this, but not sure
AI:    Let me analyze... found a better solution

Key : User's "uncertainty" gives AI permission to think independently.

4. Pattern Discovery ​

Capability : Actively identify and align with architectural patterns from the codebase.

Example:

python

# AI scans codebase, discovers pattern:

# Pattern 1: Three-state event flow
FunctionCallStartEvent → FunctionCallCompleteEvent → FunctionCallErrorEvent

# Pattern 2: Missing symmetry
StreamStartEvent → StreamCompleteEvent → ??? (missing ErrorEvent)

# Reasoning: Add StreamErrorEvent to align pattern

Value : Ensures new code is consistent with existing architecture.

5. Implicit Knowledge Transfer ​

Problem : In explicit instructions, user's reasoning process is lost.

Case:

python

# User's mental reasoning chain:
1. When HTTP fails, curl also needs to be logged
2. Can't get curl after try fails
3. So need to prepare before try
4. StreamStartEvent also needs to be yielded early

# ❌ Explicit instruction: "Move curl_command before try"
# → AI only gets conclusion, doesn't know why

# ✅ Socratic: "HTTP failed, how should curl be logged?"
# → AI is forced to rebuild reasoning chain, gains deep understanding

Principle : Rebuilding reasoning process > being told conclusion.

---

Practice Principles ​

Principle 1: Problem-oriented, not solution-oriented ​

python

# ❌ Solution-oriented
"Add Y at position X"

# ✅ Problem-oriented
"X has a problem, how to solve?"
"Logs show X failed, how to debug?"

Principle 2: Expose uncertainty ​

Core Idea : Your struggles and doubts are exactly what AI needs most.

❌ Over-confidence (pretending certainty):

 "Do A, then B, finally C"

✅ Expose struggles (real thinking process):

 "I'm thinking whether to refactor this inheritance system...
Splitting into composition pattern might be clearer, but it's a lot of work...
Should we keep the original interface? New and old coexisting might be safer?
But maintaining two sets of code is also painful...
How do you think we should balance this?"

Why are struggles better?

1. Exposes multiple alternative solutions (AI can evaluate)
2. Shows trade-off dimensions (workload vs architectural clarity)
3. Provides constraint conditions (need backward compatibility)
4. Gives AI design space (rather than forcing execution)

Effect : Your struggles force AI to perform multi-dimensional analysis, rather than blindly executing.

Principle 3: Provide context, not instructions ​

python

# ❌ Context-less instruction
"Modify line 42"

# ✅ Rich context
"Logs show NullPointerException at line 42"
"User feedback says this feature fails in edge cases"

Principle 4: Encourage exploration and verification ​

python

# ❌ Force execution
"Do as I say, don't ask why"

# ✅ Encourage questioning
"What problems do you see with this approach?"
"Is there a better implementation?"

Principle 5: Layered progression ​

Layer 1: What (what's the problem)

 "HTTP request failed"

Layer 2: Why (why it matters)

 "Need to debug, but can't get curl command"

Layer 3: How (possible direction)

 "Can we yield an event when it fails?"

---

Case Studies ​

Case: The Birth of StreamErrorEvent ​

Background ​

When HTTP request fails, curl command is not logged, making it impossible to manually reproduce the problem.

Traditional Instructive Solution (Hypothetical) ​

User: "Add error field to StreamStartEvent"

AI executes:

python

class StreamStartEvent:
    model: str
    provider: str
    error: str | None = None  # ❌ Violates single responsibility

Problems:

1. StreamStartEvent represents both "start" and "error"
2. Semantic confusion
3. Inconsistent with FunctionCallErrorEvent pattern
4. Difficult to distinguish normal/failed downstream

Socratic Solution (Actual) ​

User: "Look at the latest logs, the http failed. But I'm thinking,
      when it fails, can we also make an event? Like stream start event?
      But maybe that's not good? Or call it stream error event?
      Can we also yield the curl?"

AI Reasoning Process:

1. Understand problem domain

   • HTTP request failed
   • curl command for debugging reproduction
   • Currently curl not logged

2. Code exploration

   • Found prometheus_runner.py event handling loop
   • Discovered StreamStartEvent only yields after HTTP success
   • Checked events.py to understand event system

3. Pattern discovery

python

     # Found existing pattern:
     FunctionCallStartEvent → Complete/Error ✅
     
     # Found inconsistency:
     StreamStartEvent → Complete ❌ (missing Error)

4. Solution design

   • Create separate StreamErrorEvent (align pattern)
   • yield + raise pattern (ensure event logging and exception propagation)
   • Yield StreamStartEvent before try (ensure curl is logged)

5. Complete implementation

python

     # 1. Define event
     class StreamErrorEvent(LLMEvent):
         error_type: str
         error_message: str
         curl_command: str | None
     
     # 2. Modify handler
     async def execute(self):
         curl = self.http_debug.last_curl_command
         yield StreamStartEvent(curl=curl)
     
         try:
             stream = await create(...)
             async for event in stream:
                 yield event
         except Exception as e:
             yield StreamErrorEvent(error=str(e), curl=curl)
             raise
     
     # 3. Add formatting logic
     def _format_stream_error(self, event):
         return f"❌ Failed\n{event.error_message}\ncURL: {event.curl_command}"
     
     # 4. Write test
     async def test_stream_error_event():
         events = []
         try:
             async for e in simulate_failure():
                 events.append(e)
         except:
             pass
         assert len(events) == 2  # Start + Error

Result Comparison ​

Dimension	Instructive (Hypothetical)	Socratic (Actual)
Files modified	1	4
Architectural consistency	❌ Violates single responsibility	✅ Aligns with existing patterns
Completeness	⚠️ Only changed event definition	✅ End-to-end implementation
Testing	❌ None	✅ Includes verification
Maintainability	❌ Low	✅ High

Key Insight:

User's prompt was vague ("can we...?", "but maybe not good?"), but contained:

1. Problem scenario: "http failed"
2. Core need: "yield the curl"
3. Candidate solution: "stream error event?"
4. Technical doubt: implied "can yield and raise coexist?"

What AI did:

1. Verified user's intuition (StreamErrorEvent is right)
2. Resolved user's concerns (yield + raise is safe)
3. Supplemented missing implementations (formatting, tests, exports)

---

Deep Insights ​

Insight 1: Strategic transfer of cognitive load ​

Traditional division:

• Human: Bears design cognitive load (figure out how to do it)
• AI: Bears execution cognitive load (precisely edit code)

Problem : Human design capability is limited, may be based on incomplete information.

Socratic division:

• AI: Bears design cognitive load (understand codebase, identify patterns, propose solutions)
• Human: Bears review cognitive load (judge if solution meets needs)

Advantage : AI has complete codebase view, design capability is seriously underestimated.

Insight 2: Dimensionality difference in search space ​

Instructive: 1-dimensional search

 User instruction → unique execution path → fixed output

Socratic: N-dimensional search

 User question → can explore multiple solutions → choose optimal
          ↓
       Solution A: Modify StreamStartEvent (eval: violates SRP)
       Solution B: Add StreamErrorEvent (eval: aligns pattern ✅)
       Solution C: Use try-finally (eval: can't pass error)

Result : More likely to find globally optimal solution.

Insight 3: Essential difference in knowledge representation ​

Instructive: Explicit knowledge transfer

 "Change X to Y" → AI knows What (what to do)

Socratic: Implicit knowledge reconstruction

 "X has problem" → AI reasons Why (why) + How (how to do)

Deep understanding comes from reconstruction process, not being told.

This is similar to:

• Tell student "1+1=2" (explicit)
• vs Let student understand addition by counting apples (reconstruction)

Insight 4: Graceful degradation of failure modes ​

Instructive failure:

• Execution error (syntax, logic)
• Hard to debug (don't know why doing this)
• Cascading failure (one error causes multiple problems)

Socratic failure:

• Understanding deviation (misunderstand requirements)
• Easy to spot (has reasoning process to trace)
• Local failure (wrong solution, just re-reason)

Key : Socratic failure is more transparent, easier to correct.

Insight 5: Paradigm shift in collaboration model ​

Traditional: Human designs → AI executes

 Role: Master-tool
Characteristic: Human bears all intellectual work
Limitation: Limited by human cognitive ability

Socratic: Human questions → AI designs → Human reviews

 Role: Collaborators
Characteristic: AI bears intellectual work, human controls direction
Advantage: Leverages AI's architectural design capability

This is AI's transformation from "tool" to "collaborator".

Insight 6: Value of uncertainty ​

Certainty instruction:

 "Do X" → AI assumes user is right → blindly executes

Uncertainty questioning:

 "X might be useful? But not sure" → AI knows needs verification → independently evaluates

Key Discovery : User's exposed uncertainty actually produces more certain results.

Because:

• Uncertainty → encourages AI to question
• Questioning → deep analysis
• Deep analysis → discovers problems and better solutions

---

Best Practices Guide ​

When to use Socratic style? ​

✅ Recommended scenarios:

1. Architectural design tasks

   "Need to add error handling, how to design?"
   "How should this module be split?"
   

2. Problem diagnosis tasks

   "What's the root cause of this bug?"
   "Why did performance suddenly drop?"
   

3. Pattern alignment tasks

   "How to make this code conform to existing architecture?"
   "Are there similar implementations to reference?"
   

4. Complete feature development

   "Need to implement X feature, how to do it well?"
   "How to elegantly handle Y scenario?"
   

5. Uncertain about best solution

   "I want to use solution A, but worried about performance"
   "Which is more suitable, B or C?"
   

When to use instructive style? ​

✅ Recommended scenarios:

1. Simple mechanical modifications

   "Change variable name from foo to bar"
   "Delete line 42"
   

2. Formatting operations

   "Format this file"
   "Fix this typo"
   

3. Known certain solution

   "Add this import statement"
   "Update version number to 2.0"
   

Tips for constructing high-quality Socratic prompts ​

Tip 1: Provide problem, not solution ​

python

# ❌ Bad
"Add case StreamErrorEvent in event_recorder.py"

# ✅ Good
"When HTTP fails, how should error information be logged and displayed?"

Tip 2: Expose constraints and concerns ​

python

# ❌ Bad
"Add caching"

# ✅ Good
"Want to add caching to improve performance, but worried about memory usage. What's a good strategy?"

Tip 3: Provide contextual clues ​

python

# ❌ Bad
"Optimize this function"

# ✅ Good
"Logs show this function takes 2s, accounting for 80% of request time.
 Profile shows main time is in database queries. How to optimize?"

Tip 4: Use progressive refinement ​

python

# Round 1: Big direction
"Need to support multiple languages, how to design?"

# Round 2: Specifics (based on AI's initial solution)
"i18n solution is good, but where should config files go?"

# Round 3: Details (based on further discussion)
"JSON or YAML? How to support dynamic loading?"

Tip 5: Encourage comparison and trade-offs ​

python

# ❌ Bad
"Use Redis for caching"

# ✅ Good
"Caching can use Redis or local memory.
 Redis supports distributed but adds dependency,
 Memory cache is simple but can't cross instances.
 Which is more suitable for this scenario?"

Prompt Templates ​

Template 1: Problem diagnosis ​

[Observed phenomenon]
[Related logs/error info]
[Solutions already tried]
What might be the cause? How to solve?

Example:

 HTTP request randomly fails in production
Error log: ConnectionResetError
Already tried: Increased timeout, problem persists
What might be the cause? How to solve?

Template 2: Architectural design ​

Requirement: [Feature to implement]
Constraints: [Performance/compatibility/maintainability requirements]
Questions: [Uncertain points]
How to design this well?

Example:

 Requirement: Support error handling for streaming LLM calls
Constraints: Need to log debug info, can't affect existing exception propagation
Questions: How to yield event on error? Will it conflict with raise?
How to design this well?

Template 3: Pattern alignment ​

Discovery: [Observed situation]
Problem: [Inconsistency with existing code]
Reference: [Existing similar implementations]
How to align?

Example:

 Discovery: Newly added StreamStartEvent only has start and complete
Problem: FunctionCall has start/complete/error three states
Reference: FunctionCallErrorEvent implementation
How to align?

Common Anti-patterns ​

Anti-pattern 1: Pseudo-Socratic ​

python

# ❌ Seems to ask, actually commanding
"Don't you think we should add try-except here?"

# ✅ True Socratic
"This might throw an exception, how to handle it best?"

Anti-pattern 2: Over-ambiguous ​

python

# ❌ Too vague, lacks context
"Optimize a bit"

# ✅ Vague but has direction
"This function is slow under high concurrency, profile shows most time waiting for locks.
 What optimization ideas?"

Anti-pattern 3: Assuming AI knows hidden context ​

python

# ❌ Assumes AI knows business context
"Implement according to previously discussed plan"

# ✅ Re-provide context
"We previously discussed using event-driven architecture.
 Now implementing error handling, how to do it based on this architecture?"

Anti-pattern 4: Premature optimization instruction ​

python

# ❌ Instruction based on wrong assumption
"Change this list to set, improve query performance"
(Maybe list needs to maintain order)

# ✅ Describe problem, let AI analyze
"This query is slow, data volume is 100K. How to optimize?"

---

Summary ​

Core Points ​

1. Vague prompts often produce higher quality code

   • Because they activate AI's architectural design capability
   • Rather than mechanical code editing capability

2. Essence of Socratic AI Coding is trust inversion

   • From "Human designs → AI executes"
   • To "AI designs → Human reviews"

3. Five core mechanisms

   • Avoid over-constraint
   • Force deep understanding
   • Trust inversion
   • Pattern discovery
   • Implicit knowledge transfer

4. Best practices

   • Problem-oriented, not solution-oriented
   • Expose uncertainty
   • Provide context
   • Encourage exploration
   • Layered progression

Philosophical Reflection ​

Socrates said: "I know that I know nothing."

In AI Coding, this means:

When humans admit "I'm not sure of the best solution", they actually get better code.

Because:

• Admit uncertainty → give AI thinking space
• AI thinking → deep understanding of codebase
• Deep understanding → discover better solutions
• Better solutions → high quality code

This is a paradox, and also an insight:

Knowing that you don't know is the beginning of gaining knowledge.

Future Outlook ​

Socratic AI Coding is not just a technical method, but a collaboration philosophy :

• AI is not a tool, but a thinking partner
• Human is not a designer, but a questioner and reviewer
• Programming is not instruction execution, but dialogue and exploration

The maturity of this paradigm will redefine the relationship between humans and AI:

From master-tool, to collaborators.

---

Appendix ​

References ​

1. Socratic Method - Stanford Encyclopedia of Philosophy
2. The Art of Asking Questions - Elenchus and Maieutics
3. Pattern Languages in Software Architecture - Christopher Alexander
4. Prompt Engineering for Large Language Models - Recent Advances

Related Resources ​

• Sequential Thinking MCP - Deep reasoning tool
• Architecture Decision Records - Best practice for recording design reasoning

Author's Note ​

This document is distilled from real project experience.

The StreamErrorEvent design in the case is a practical result of Socratic AI Coding.

Welcome to validate and improve these theories in practice.

---

Appendix: Classic Struggle Cases Collection ​

"Struggle is the outward manifestation of thinking, and thinking is the prerequisite for high-quality code."

This chapter collects particularly struggling but produced particularly good results prompt cases from real projects.

Case 1: Philosophical Struggle of Naming ​

User's struggle:

 "Should this concept be called workflow or graph?
workflow is closer to business, easier for users to understand...
But technically it's really a directed acyclic graph, graph is more accurate...
Should we rename it? But renaming has a big impact, API all needs to change...
Or just change internal implementation, keep workflow externally?
But this internal-external inconsistency will confuse maintainers...
How do you think we should balance this? Is there a best of both worlds?"

AI's reasoning process:

1. Analyze semantic differences of two terms
2. Assess impact scope (API, docs, user mental model)
3. Find industry precedents (Airflow uses DAG, Temporal uses Workflow)
4. Propose progressive solution:
   • Internal rename to GraphEngine (technically accurate)
   • Keep workflow terminology externally (user friendly)
   • Explain relationship in docs

Why did struggle produce good solution?

• Exposed multiple evaluation dimensions (accuracy, compatibility, maintainability)
• Showed constraint conditions (API stability)
• Allowed AI to find creative compromise

Result:

• ✅ Technically accurate (internal uses Graph)
• ✅ User friendly (external uses Workflow)
• ✅ Backward compatible (API unchanged)

---

Case 2: Painful Choice of Architecture Refactoring ​

User's struggle:

 "I found this inheritance system is a bit complex, too many subclasses...
Want to split into composition pattern, might be clearer...
But it's a lot of work, need to change dozens of files...
Should we keep the original base class? New and old code coexisting might be safer?
But then we have two implementations, higher maintenance cost...
And new people will be confused: why are there two ways?
All-in complete refactoring? Risk is too high, if there's a bug...
Progressive migration? But dragging time too long, technical debt still exists...
Really struggling, what's your suggestion?"

AI's analysis:

1. Assess current inheritance depth and complexity
2. Identify core benefits of refactoring (testability, extensibility)
3. Analyze risk points (regression test coverage, critical paths)
4. Propose phased plan:
   • Phase 1 : First add composition version (don't delete inheritance)
   • Phase 2 : New features use composition, old code unchanged
   • Phase 3 : Increase test coverage
   • Phase 4 : Gradually migrate key modules
   • Phase 5 : Deprecate old base class (set deprecation warning)

Why was struggle valuable?

• Listed pros/cons of all solutions (complete refactoring vs progressive vs dual-track)
• Exposed real constraints (risk, time, maintenance cost)
• Forced AI to design a solution balancing all aspects

Result:

• ✅ Reduce risk (progressive)
• ✅ Control debt (has clear end state)
• ✅ Team acceptable (time controllable)

---

Case 3: Design Struggle of Prompt Layering ​

User's struggle:

 "I'm looking at OpenAI and Claude's APIs, found they handle system prompt differently...
Want to unify, but don't know how to layer...
One layer? system_prompt + user_prompt? Simple is simple, but not flexible enough...
Two layers? But Claude has cache, to utilize caching, maybe need three layers?
L1: system (fixed)
L2: context (semi-fixed, cacheable)
L3: execution (variable, not cached)
But three layers might be over-designed? Do we really need this complexity?
And does OpenAI support it? If not, how to downgrade?
Or keep it simple, two layers enough, deal with performance issues when they come?
But then changing costs more...
What do you think we should design?"

AI's reasoning:

1. Compare OpenAI and Claude's caching mechanisms
   • OpenAI: automatic prefix caching (>=1024 tokens)
   • Claude: manual cache_control marking
2. Analyze benefits of three-layer architecture
   • L1 fixed → 100% cache hit rate
   • L2 semi-fixed → ~80% cache hit rate
   • L3 variable → not cached, keep flexibility
3. Assess implementation cost
   • OpenAI auto caches, three layers same cost as two
   • Claude needs manual marking, but framework can encapsulate
4. Propose solution:
   • Adopt three-layer architecture (future-oriented)
   • Framework layer handles uniformly (transparent to upper layer)
   • OpenAI merges L2+L3 (auto downgrade)

Why was this struggle particularly good?

• Showed complexity of technical selection (multi-vendor differences)
• Exposed trade-off points (simplicity vs performance vs future expansion)
• Included "fear of over-design" real concern
• Proposed "wait until problem appears" alternative thinking

Result:

• ✅ Adopted three-layer architecture
• ✅ Framework auto downgrades (OpenAI merges L2+L3)
• ✅ Actual test cache saves 40-60% cost

---

Case 4: Paradigm Struggle of Error Handling ​

User's struggle:

 "A bit struggling about error handling here...
Throwing exceptions is simple, but caller has to write a bunch of try-catch, annoying...
Returning Result<T, E> type is more elegant, Rust does this...
But we're Python, Result type isn't in standard library...
To use it we need to implement ourselves or bring in third-party library...
And changing has big impact, all callers need to change...
Should we support both? Can be backward compatible...
But then codebase has two styles...
New people will be confused: when to use exception, when to use Result?
Or don't change? Wait until Python 3.14 has Result type?
But that's waiting until when...
Really struggling, what do you think?"

AI's analysis:

1. Compare pros/cons of exception vs Result
   • Exception: clear control flow, but abuse hides problems
   • Result: forces error handling, but calling is tedious
2. Analyze Python ecosystem status
   • Returns library provides Result type
   • But not standard, limited acceptance
3. Check project status
   • Already have 50+ exception throw points
   • Most errors are recoverable
4. Propose hybrid solution:
   • Unrecoverable errors : continue using exceptions
   • Recoverable errors : return Optional[T] or tuple[T, Error | None]
   • Avoid bringing in third-party Result type (reduce dependencies)

Why is this struggle typical?

• Involves language philosophy (Python isn't Rust)
• Has realistic constraints (migration cost, team habits)
• Includes "wait for official support" avoidance psychology
• Worried about introducing two standards

Result:

• ✅ Hybrid solution (exception + Optional)
• ✅ Wrote ADR document explaining decision reasoning
• ✅ Team understands and accepts

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Case 5: Performance Optimization Trade-off Struggle ​

User's struggle:

 "Profile shows this function is slow, accounts for 60% execution time...
Can optimize with caching, but will increase memory usage...
Or use async, but code complexity rises...
Could also use C extension, but maintenance cost too high...
Problem is, it's not particularly slow now, about 500ms...
Optimizing to 100ms meaningful? Can users perceive the difference?
And this feature isn't called frequently, just hundreds of times a day...
Optimization benefit might not be worth the time investment...
But if concurrency goes up later, might become bottleneck...
Should we optimize ahead? Or wait until really slow?
Follow "don't prematurely optimize" principle?
But then changing costs more...
How do you think we should judge?"

AI's analysis:

1. Quantify benefits
   • Current: 500ms × 600 times/day = 5 minutes/day
   • After optimization: 100ms × 600 times/day = 1 minute/day
   • Saved: 4 minutes/day (negligible)
2. Assess future growth
   • Check historical data: call volume averages 20% monthly growth
   • Estimate: 6 months later reaches 2000 times/day (still not high)
3. Identify real problem
   • 500ms isn't bottleneck, bottleneck is another 2s query
4. Propose solution:
   • Don't optimize this function now (low ROI)
   • Optimize that 2s query (high priority)
   • Add performance monitoring (timely discover real bottlenecks)

Why was this struggle good?

• Exposed "premature optimization" concern
• Included multiple optimization solutions and their costs
• Provided quantified data (500ms, 600 times/day)
• Expressed uncertainty about future

Result:

• ✅ Correct priority (optimize 2s query first)
• ✅ Saved time (didn't do useless work)
• ✅ Established monitoring (data-driven decisions)

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Case 6: Progressive Struggle of Type Safety ​

User's struggle:

 "Want to add type annotations to this module, improve type safety...
But now code has many Any types, changing is huge workload...
And some places really hard to define types, like this dynamic config...
Should we use generics? Type safety is good, but code complexity shoots up...
New people seeing a bunch of TypeVar, Generic will be confused...
Or keep Any? Simple is simple, but mypy can't check problems...
Runtime only discovers errors, debugging cost higher...
Is there a middle solution? Part use generics, part use Any?
But this is inconsistent...
Progressive migration? First change core modules, others slowly?
But "slowly" often becomes "never"...
How do you think we should balance type safety and development efficiency?"

AI's analysis:

1. Assess value of type safety
   • Core data structures: high value (prevent runtime errors)
   • Utility functions: medium value
   • Test code: low value
2. Identify difficulties
   • Dynamic config really hard to type precisely
   • Can use TypedDict or Protocol to partially constrain
3. Propose layered strategy:
   • L1 Core : strict types (Pydantic Models)
   • L2 Business : progressive types (critical path first)
   • L3 Tools : loose types (use # type: ignore to mark known issues)
4. Tool support:
   • Enable mypy strict mode (but allow exceptions)
   • Use pyright's reportUnknownVariableType warning

Why is this struggle typical?

• Reflects "all or nothing" mentality (either change all or don't change)
• Worried about inconsistency (but reality often needs compromise)
• Afraid of "never finishing" (common psychological barrier to progressive approach)

Result:

• ✅ Layered strategy (core strict, peripheral loose)
• ✅ 3 months completed core module typing
• ✅ Found and fixed 5 potential bugs

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Case 7: HTTP Failure Debug Info Struggle (Main Case) ​

User's struggle:

 "Look at the latest logs, the http failed...
But I'm thinking, when it fails, can we also make an event?
Like stream start event? But maybe that's not good?
Or call it stream error event?
Can we also yield the curl?
But can yield and raise coexist? Will there be problems?
If yield error event, do we still need to raise exception?
If not raise, how does caller know it failed?
If raise, will event be lost?
Or only yield not raise? But this breaks original error handling...
Really a bit dizzy... can you help me sort out the thinking?"

AI's reasoning:

1. Understand core need: log curl for debugging
2. Identify existing pattern: FunctionCallErrorEvent already exists
3. Design StreamErrorEvent aligned with pattern
4. Verify yield + raise safety (Python guarantees)
5. Propose complete solution (see main case)

Why did this struggle produce excellent design?

• Exposed multiple technical questions (can yield + raise coexist?)
• Showed design intuition ("maybe not good")
• Included multiple candidate solutions (stream start vs error event)
• Genuinely expressed confusion ("a bit dizzy")

Result:

• ✅ StreamErrorEvent design elegant
• ✅ yield + raise pattern safe
• ✅ curl command successfully logged
• ✅ Became classic case of Socratic AI Coding

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Common Patterns in Struggle Cases ​

Analyzing above cases, we find common characteristics of high-quality struggles:

1. Multi-dimensional trade-offs ​

Not: A or B?
But: A's advantage is X, but disadvantage is Y; B's advantage is Z, but disadvantage is W.
     In this scenario, which is more suitable?

2. Expose constraint conditions ​

Not: Do X
But: Want to do X, but limited by Y (compatibility/performance/time/team level)

3. Admit uncertainty ​

Not: I think should do this
But: I think this, but maybe not good? What do you think?

4. Show thinking process ​

Not: Give me solution
But: I thought of A, B, C three solutions, A's problem is..., B's problem is...,
     C looks okay but I'm worried...

5. Real emotional expression ​

Not: (calm technical analysis)
But: Really struggling..., a bit dizzy..., very worried..., don't know...

The Art of Struggle ​

Key Insight :

**Struggle is not weakness, but a sign of deep thinking.**Your struggle level often correlates with problem complexity.

Practical Suggestions :

1. Don't pretend certainty - If you're unsure, say unsure
2. List all solutions - Even if you think some are infeasible
3. Expose your worries - This is exactly what AI needs to analyze
4. Say your confusion - "A bit dizzy" is more valuable than "I'm certain"
5. Show trade-off process - This is the core of design

Meta-insight :

The more you struggle, the better AI's output often is.

Because:

• Struggle = deep thinking
• Deep thinking = exposed complexity
• Exposing complexity = gave AI analysis space
• AI analysis = high quality solution

So: don't fear struggle, embrace struggle.

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"The best code comes from the best questions."

"The best questions often come from the most real struggles."