PromptChainer: Visual Programming으로 LLM 프롬프트 체이닝하기

# LLM 체인 단계별 디버깅 패턴 예시 (Python pseudocode)

def run_chain_with_debug(input_text, breakpoint_node=None):
    results = {}
    
    # Step 1: 입력 분류
    classification = llm_call(
        prompt=f"""[Dialog] Play some music I like. [Class] is_music
[Dialog] Hey what's up. [Class] not_music
[Dialog] {input_text} [Class]"""
    )
    results['classify'] = classification
    print(f"[Node 1] Classification: {classification}")
    
    # 브레이크포인트: 중간 출력 수동 수정 가능
    if breakpoint_node == 'classify':
        classification = input(f"현재 출력: {classification}\n수정할 값 입력 (엔터=유지): ") or classification
    
    # Step 2: 분기 처리
    if classification.strip() == 'is_music':
        # 음악 관련 처리
        entities = llm_call(
            prompt=f"Extract music entities from: {input_text}\nEntities:"
        )
        results['entities'] = entities
        print(f"[Node 2] Entities: {entities}")
        
        # Step 3: 헬퍼 노드 - 파싱
        entity_list = [e.strip() for e in entities.split(',')]
        
        # Step 4: 최종 응답 생성
        response = llm_call(
            prompt=f"Generate a friendly music recommendation response for: {entity_list[0]}"
        )
    else:
        response = llm_call(
            prompt=f"Generate a casual non-music response to: {input_text}"
        )
    
    results['final'] = response
    return results

# 사용 예시
result = run_chain_with_debug("i love the song by Sting", breakpoint_node='classify')