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  3. Retrieval Augmented Generation (RAG) with LangChain
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Exercise

String evaluation

Time to really evaluate the final output by comparing it to an answer written by a subject matter expert. You'll use LangSmith's LangChainStringEvaluator class to perform this string comparison evaluation.

A prompt_template for string evaluation has already been written for you as:

You are an expert professor specialized in grading students' answers to questions.
You are grading the following question:{query}
Here is the real answer:{answer}
You are grading the following predicted answer:{result}
Respond with CORRECT or INCORRECT:
Grade:

The output from the RAG chain is stored as predicted_answer and the expert's response is stored as ref_answer.

All of the necessary classes have been imported for you.

Инструкции

100 XP
  • Create the LangSmith QA string evaluator using the eval_llm and prompt_template provided.
  • Evaluate the RAG output, predicted_answer, by comparing it with the expert's response to the query, which is stored as ref_answer.