Using output_parser in LangChain The output_parser is responsible for processing the LLM's response — it should not be used directly with the output from a prompt alone. If you want to combine a prompt, an LLM, and an output parser, the correct way is to chain them together:

python Copy Edit from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser

Initialize components

llm = ChatOpenAI(model="gpt-4o-mini") prompt = ChatPromptTemplate.from_template("Tell me a short joke about {topic}") output_parser = StrOutputParser()

Combine prompt → LLM → output parser

chain = prompt | llm | output_parser

Run the chain

result = chain.invoke({"topic": "programming"}) print(result) ❌ Incorrect: Passing prompt.invoke() output directly into an output_parser (the parser expects an LLM response, not a prompt output).

Creating a Prompt Template for RAG In Retrieval-Augmented Generation (RAG), you can guide the LLM to only use the provided context. This helps reduce hallucinations and keeps answers grounded in real data.

python Copy Edit from langchain_core.prompts import ChatPromptTemplate

template = """Answer the question based only on the following context: {context}

Question: {question}

Answer:"""

prompt = ChatPromptTemplate.from_template(template) Why this matters: By instructing the model to only answer based on the context, you improve accuracy and reliability in RAG pipelines.

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