Agentes de autocorreção e reflexão
Aprenda a criar agentes capazes de avaliar criticamente suas próprias saídas e aprimorar iterativamente suas respostas ou ações.
Agentes de autocorreção e reflexão é uma aula grátis de AI Agents with LangChain & Autonomous Workflows no CoddyKit. Esta é a aula 3 de 6. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de AI Agents with LangChain & Autonomous Workflows, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de AI Agents with LangChain & Autonomous Workflows inclui 6 aulas no total.
Partes desta aula ainda não foram traduzidas e aparecem em inglês.
Agents That Think Twice
Ever wished your AI agent could check its own work? That's exactly what self-correction and reflection agents do!
These agents are designed to not just generate an output, but also to critically evaluate it and make improvements. Think of it as an agent having an internal "editor" or "critic".
The Reflective Cycle
The core idea of reflection is an iterative process:
- Generate: The agent produces an initial response or action.
- Reflect: It then uses another prompt (or a different LLM) to analyze its own output against specific criteria.
- Refine: Based on the reflection, the agent adjusts its original output, aiming for better quality or accuracy.
This cycle can repeat multiple times until a satisfactory result is achieved.
Inside a Reflection Agent
A self-correcting agent typically involves a few key parts:
- Generator LLM: The primary Large Language Model that creates the initial output.
- Critic/Reflector LLM: Often the same LLM, but prompted to act as a critic, evaluating the generator's output.
- Reflection Prompt: A carefully designed prompt that guides the critic LLM on what to evaluate and how.
- Refinement Mechanism: The logic that takes the critic's feedback and uses it to improve the next generation.
The Generator Prompt
Just like any LLM interaction, a clear initial prompt for the "generator" is crucial. This prompt tells the agent what task to perform.
For example, if we want an agent to write a short story, the generator prompt would define the story's theme, characters, and length. The goal is a solid first draft.
prompt_template = "Write a 3-sentence story about a brave knight and a dragon."Crafting the Reflection Prompt
The reflection prompt is where the magic happens. It instructs the LLM to review the previous output and identify areas for improvement.
This prompt should include specific criteria. For our story example, it might ask: "Is the story engaging? Does it clearly mention a knight and a dragon? Is it exactly 3 sentences?"
reflection_prompt = "Critique the following story: '{story}'. Does it meet these criteria: 1. Engaging? 2. Knight and Dragon present? 3. Exactly 3 sentences? Provide specific feedback for improvement."Simple Reflection Example
Let's trace our story example conceptually:
- Generate: LLM writes an initial story draft.
- Reflect: LLM reads the story and the reflection prompt, then gives feedback (e.g., "It's 4 sentences, not 3.").
- Refine: The agent receives the feedback. It then tries to rewrite the story, incorporating the feedback (e.g., shortening it to 3 sentences).
This loop continues until the story meets all criteria or a limit is reached.
Building a Refinement Loop
In practice, the refinement process often involves a loop. The agent continues to generate and reflect until a set condition is met. This condition could be:
- A maximum number of iterations.
- The reflection feedback indicates no further improvements are needed.
- A specific quality metric is achieved.
This iterative process allows for continuous self-improvement, making agents more robust.
Code: A Simple Reflection Loop
Here's a simplified Python example demonstrating a basic self-correction loop. We'll use a placeholder LLM for clarity, focusing on the generate, reflect, and refine logic.
class MockLLM:
def __init__(self):
pass
def generate_initial_output(self, task_prompt):
# Simulate a generator LLM that sometimes gets it wrong
if "start with 'Hello'" in task_prompt:
return "Greetings! How are you?" # Incorrect initial attempt
return "Hello World!" # Correct initial attempt based on prompt
def reflect_and_suggest(self, original_task, generated_output):
# Simulate a critic LLM
reflection_prompt = (
f"Given the task: '{original_task}'. "
f"The generated output was: '{generated_output}'. "
"Critique this output: Does it start with 'Hello'? "
"If not, suggest a correction. Be concise."
)
# Simplified reflection logic for the mock LLM
if not generated_output.strip().startswith("Hello"):
return "Doesn't start with 'Hello'. Suggestion: Start with 'Hello'."
return "Output meets criteria."
# --- Main Logic ---
llm = MockLLM()
task = "Create a greeting sentence that starts with 'Hello'."
current_output = llm.generate_initial_output(task)
print(f"Initial Output: {current_output}")
for i in range(3): # Max 3 attempts (initial + 2 refinements)
reflection = llm.reflect_and_suggest(task, current_output)
print(f"Reflection {i+1}: {reflection}")
if "Output meets criteria" in reflection:
print("Agent self-corrected successfully!")
break
else:
# Simulate applying the suggestion
if "Start with 'Hello'" in reflection and not current_output.startswith("Hello"):
current_output = "Hello" + current_output[current_output.find(' '):] # Simple fix
print(f"Refined Output: {current_output}")
else:
print("No specific refinement strategy for this feedback. Stopping.")
break
print("\nFinal Output:", current_output)Benefits & Trade-offs
Reflection agents are powerful for tasks requiring high accuracy or complex reasoning, especially when a single pass might miss nuances.
- Improved Accuracy: Reduces errors and hallucinations.
- Robustness: Handles complex prompts better.
- Quality: Leads to higher-quality outputs.
However, they increase computational cost and latency due to multiple LLM calls. Use them where quality justifies the overhead.
Test Your Knowledge
Consider an agent designed to summarize a document and then self-correct. What is the primary purpose of the "reflection prompt" in this scenario?
Recap: Self-Correction
We've explored how self-correction and reflection agents work. These agents enhance AI capabilities by allowing them to critically evaluate and refine their own outputs.
- They follow a generate, reflect, refine cycle.
- Key components include a generator, a critic, and specific reflection prompts.
- While increasing cost and latency, they significantly boost output quality and accuracy for complex tasks.
This ability to "think twice" is a crucial step towards more robust and intelligent AI systems!
Perguntas Frequentes
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Aprenda a criar agentes capazes de avaliar criticamente suas próprias saídas e aprimorar iterativamente suas respostas ou ações. Você pratica AI Agents with LangChain & Autonomous Workflows com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.
Preciso ter experiência prévia para começar AI Agents with LangChain & Autonomous Workflows?
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Quanto tempo leva a aula “Agentes de autocorreção e reflexão”?
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Todas as aulas deste curso
- Agentes ReAct e de planejamento e execução
- Designs hierárquicos de agentes
- Agentes de autocorreção e reflexão
- Arquiteturas cognitivas para agentes
- Padrões de colaboração entre múltiplos agentes
- Sistemas híbridos de agentes