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langchain - 💡(How to fix) Fix Improve RAG prompt quality for better performance with smaller HuggingFace models [4 comments, 3 participants]

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langchain-ai/langchain#36226•Fetched 2026-04-08 01:31:17
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Checked other resources

  • This is a feature request, not a bug report or usage question.
  • I added a clear and descriptive title that summarizes the feature request.
  • I used the GitHub search to find a similar feature request and didn't find it.
  • I checked the LangChain documentation and API reference to see if this feature already exists.
  • This is not related to the langchain-community package.

Package (Required)

  • langchain
  • langchain-openai
  • langchain-anthropic
  • langchain-classic
  • langchain-core
  • langchain-model-profiles
  • langchain-tests
  • langchain-text-splitters
  • langchain-chroma
  • langchain-deepseek
  • langchain-exa
  • langchain-fireworks
  • langchain-groq
  • langchain-huggingface
  • langchain-mistralai
  • langchain-nomic
  • langchain-ollama
  • langchain-openrouter
  • langchain-perplexity
  • langchain-qdrant
  • langchain-xai
  • Other / not sure / general

Feature Description

Improve the prompt templates used in LangChain RAG examples to enhance answer accuracy, especially for smaller HuggingFace models like flan-t5-small or flan-t5-large. The current prompts often lead to vague responses like “I don’t know” or “Not found in document” even when the information exists in the source PDF.

Use Case

Students, developers, and AI enthusiasts using LangChain’s RAG examples want reliable answers from PDFs. Smaller models (CPU-friendly) are preferred for local testing, but they struggle with vague prompts. This improvement ensures better outputs without requiring large models or cloud resources.

Proposed Solution

Update the prompt template for RAG examples with: Clear instructions to use only the context provided. Explicit fallback for missing information (“If answer not in document, respond: ‘Not found in document’”). Structured response format for clarity. Optional: provide example templates in the documentation.

Alternatives Considered

Increasing model size (e.g., flan-t5-xl) – works, but is CPU/memory intensive. Post-processing the answers – less effective, more complex. Using external LLM APIs (OpenAI/Claude) – defeats the purpose of offline/local usage.

The prompt improvement is simple, free, and backward-compatible with all models.

Additional Context

Tested on pdf_rag_pipeline.py using a sample.pdf. Observed: smaller models often answer “I don’t know” despite clear answers in the PDF. Updating the prompt resolves this and ensures RAG works well even on local machines.

extent analysis

Fix Plan

To improve the prompt templates used in LangChain RAG examples, follow these steps:

  • Update the prompt template to include clear instructions to use only the context provided.
  • Add an explicit fallback for missing information.
  • Implement a structured response format for clarity.

Example code snippet in Python:

from langchain import LLMChain, PromptTemplate

# Define the updated prompt template
template = PromptTemplate(
    input_variables=["context", "question"],
    template="Given the context: {context}. Answer the question: {question}. If the answer is not in the document, respond with 'Not found in document'.",
)

# Create an LLM chain with the updated prompt template
chain = LLMChain(llm=llm, prompt=template)

# Test the updated prompt template
context = "Sample context from a PDF."
question = "What is the main topic of the PDF?"
output = chain({"context": context, "question": question})
print(output)

Verification

To verify that the fix worked, test the updated prompt template with various inputs and models, including smaller models like flan-t5-small. Check that the outputs are more accurate and reliable, and that the model is able to retrieve information from the context when available.

Extra Tips

  • Provide example templates in the documentation to help users understand how to use the updated prompt template.
  • Consider adding additional tests to ensure that the updated prompt template works correctly with different models and inputs.
  • Monitor user feedback and adjust the prompt template as needed to further improve answer accuracy.

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#api#ssr#installation#tensor shape#prompt template

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