Using Verbose Flag

In this guide, you’ll learn how to turn on the verbose flag for an LLMChain instance to inspect its internal steps—prompt formatting, input handling, and output generation—without enabling global debug mode.

Why Use verbose=True?

By setting verbose=True on a specific chain, you can:

• Trace how input variables populate your prompt.
• See intermediate formatting before the LLM is called.
• Log only the components you’re interested in, keeping other logs clean.

Step-by-Step Example

from langchain.chains import LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate

# 1. Define a prompt template with system and human messages
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a {subject} teacher"),
    ("human", "Tell me about {concept}")
])

# 2. Initialize the ChatOpenAI model
llm = ChatOpenAI()

# 3. Create an LLMChain with verbose logging enabled
chain = LLMChain(llm=llm, prompt=prompt, verbose=True)

# 4. Run the chain with inputs
result = chain.invoke({"subject": "physics", "concept": "galaxy"})
print(result)

Sample Console Output

The chain prints a JSON-like debug trace along with your final output:

{
  "subject": "physics",
  "concept": "galaxy",
  "text": "Galaxies are massive systems of stars, stellar remnants, interstellar gas, dust, dark matter, and other astronomical objects bound together by gravity. They come in a variety of shapes and sizes..."
}

Typical debug messages include:

• Entering LLMChain with input arguments.
• Formatted prompt after variable substitution.
• Exiting LLMChain with generated output.

Verbose vs. Global Debug

Next Steps: Callbacks

Once you’ve mastered verbose logging, you can explore callbacks to programmatically capture and process chain events for metrics, custom monitoring, or advanced debugging.

References

• LangChain LLMChain Documentation
• ChatOpenAI Model Reference
• Working with Prompt Templates