1Z0-1127-25 LangChain and AI Application Development Practice Question
A team is using LangChain's ConversationalRetrievalChain with ConversationBufferMemory to build a chatbot. After a few turns, the chatbot starts repeating information from earlier messages. What is the MOST likely cause?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The conversation history accumulates in the prompt, causing the model to focus on past messages
ConversationBufferMemory stores the entire conversation history, and each new query appends that history to the prompt. As history grows, the LLM may receive redundant context, causing repetition. Using ConversationSummaryMemory or limiting history length can mitigate this.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The conversation history accumulates in the prompt, causing the model to focus on past messages
Why this is correct
Full history in the prompt can cause the model to rephrase or repeat past content.
- ✗
The chunk_size in the text splitter is too large
Why it's wrong here
Chunk size affects retrieval quality, not conversation repetition.
- ✗
The LLM's max_tokens parameter is set too low
Why it's wrong here
A low max_tokens truncates responses but does not directly cause repetition.
- ✗
The retriever's similarity search returns irrelevant chunks
Why it's wrong here
Irrelevant chunks would cause off-topic answers, not repetition of earlier information.
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