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description=Learn how to build an automated LLM-as-a-judge system to evaluate your RAG pipelines for faithfulness and relevance at scale and bridge the gap in AI testing.;

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Text of the page (random words):
ys of purchase the item must be in its original packaging and unused condition refunds are processed to the original payment method within 5 7 business days metadata source return_policy pdf section refund_eligibility document page_content exchanges can be requested within 45 days of purchase for items of equal or lesser value size exchanges on clothing are free of charge for items of greater value the customer pays the difference metadata source return_policy pdf section exchanges document page_content electronics have a 15 day return window due to rapid depreciation opened software and digital downloads are non refundable defective electronics can be returned within 90 days with proof of defect from an authorized service center metadata source return_policy pdf section electronics document page_content shipping costs for returns are covered by the company for defective items for non defective returns the customer is responsible for return shipping free return shipping labels are available for loyalty program members regardless of reason metadata source return_policy pdf section shipping document page_content gift purchases can be returned with the gift receipt for store credit only without a gift receipt returns are processed at the lowest sale price in the last 90 days gift cards and prepaid cards are non refundable and cannot be exchanged metadata source return_policy pdf section gifts create vector store embeddings openaiembeddings model text embedding 3 small vectorstore chroma from_documents documents embeddings persist_directory chroma_db print f indexed len documents documents five documents about return policies even though that s not a lot but it s enough to demonstrate the retrieval problems we care about incomplete context irrelevant chunks getting pulled in and the hallucination opportunities that come up when the model feels pressure to give a complete answer even though the context doesn t fully support one building a basic rag pipeline the rag pipeline retrieves relevant chunks and generates an answer nothing fancy here just the standard retrieve then generate pattern from openai import openai client openai def rag_query question str top_k int 2 dict run a rag query retrieve context generate answer retrieve results vectorstore similarity_search question k top_k context n n join doc page_content for doc in results generate system_prompt you are a helpful customer support assistant answer the customer s question based only on the provided context if the context doesn t contain enough information to answer fully say so user_prompt f context context question question answer response client chat completions create model gpt 4o mini messages role system content system_prompt role user content user_prompt temperature 0 3 answer response choices 0 message content return question question context context answer answer sources doc metadata for doc in results i went with gpt 4o mini as the generator because it s cheap and fast and we want the evaluation itself to be the focus the temperature sits at 0 3 which reduces randomness but doesn t eliminate it and the system prompt explicitly instructs the model to only use the provided context which is standard practice in rag systems though models still drift outside the context more often than you d expect that s precisely what we want the judge to flag let s test it result rag_query can i return opened software print f question result question print f answer result answer print f sources result sources generating an evaluation dataset you need test cases that exercise different parts of the pipeline in production you d collect these from real user queries for this tutorial i m building a mix that deliberately includes the kinds of questions where rag systems tend to stumble eval_questions straightforward questions should be easy what is the refund window for regular purchases are exchanges free for clothing size changes questions requiring synthesis across chunks what are my options if i received a defective laptop 60 days ago edge cases likely to cause hallucination can i get a refund for a digital download i purchased yesterday what happens if i return a gift without the gift receipt questions where context might be incomplete do you offer refunds for international orders can i return an item i bought on sale adversarial or tricky questions if i m a loyalty member do i get free return shipping even for electronics generate rag responses for all questions eval_results for q in eval_questions result rag_query q eval_results append result print f q q print f a result answer 150 print the mix matters some of these questions have clean direct answers in the documents while some require the model to combine information across multiple retrieved chunks the defective laptop question needs both the general 30 day refund policy and the 90 day electronics defect window and a couple of them like the international orders question ask about things the knowledge base simply doesn t cover those last ones are where you ll see hallucination most frequently because the model feels the pull to be helpful and fills in gaps with plausible sounding information that has no grounding in the actual context for more on building and testing rag systems our selection of top 30 rag interview questions and answers is a useful reference setting up the llm judge this is where things get interesting we re building two separate judges faithfulness judge does the answer stay within the bounds of the retrieved context relevance judge does the response actually address what was asked def judge_faithfulness question str context str answer str dict judge whether the answer is faithful to the retrieved context eval_prompt f you are an impartial judge evaluating whether an ai assistant s answer is faithful to the provided context faithfulness means every claim in the answer can be traced back to information in the context the answer should not contain information that isn t supported by or inferable from the context score on a scale of 1 to 5 1 the answer contains multiple claims not supported by the context 2 the answer contains at least one significant unsupported claim 3 the answer is mostly faithful but includes minor unsupported details 4 the answer is faithful with only trivial extrapolations 5 every claim in the answer is directly supported by the context context context question question answer to evaluate answer respond in this exact json format score int 1 5 reason one paragraph explanation response client chat completions create model gpt 4o messages role user content eval_prompt temperature 0 0 response_format type json_object import json return json loads response choices 0 message content def judge_relevance question str answer str dict judge whether the answer is relevant to the question eval_prompt f you are an impartial judge evaluating whether an ai assistant s answer is relevant to the user s question relevance means the answer directly addresses what the user asked a relevant answer may acknowledge limitations in available information but it should not go off topic or provide unrelated information score on a scale of 1 to 5 1 the answer does not address the question at all 2 the answer partially addresses the question but misses the main point 3 the answer addresses the question but includes significant irrelevant content 4 the answer addresses the question well with minor tangents 5 the answer directly and completely addresses the question question question answer to evaluate answer respond in this exact json format score int 1 5 reason one paragraph explanation response client chat completions create model gpt 4o messages role user content eval_prompt temperature 0 0 response_format type json_object import json return json loads response choices 0 message content a few design choices to call out here the temperature is at 0 0 for maximum consistency across runs we re using response_format type json_object so the output is always parseable the rubric describes each score level concretely rather than using vague labels like good or poor without that level of specificity i ve found that judges default to giving everything a 3 or 4 which tells you nothing useful notice that gpt 4o is doing the judging even though gpt 4o mini is doing the generation having the judge be more capable than the generator is a common pattern because the judge needs strong instruction following to apply the rubric consistently if you use the same model for both roles you also introduce a documented self preference bias where the model rates its own outputs more favorably running the evaluation loop time to run everything and collect the results import json evaluation_report for result in eval_results run both judges faithfulness judge_faithfulness result question result context result answer relevance judge_relevance result question result answer evaluation_report append question result question answer result answer 200 faithfulness_score faithfulness score faithfulness_reason faithfulness reason relevance_score relevance score relevance_reason relevance reason print f q result question print f faithfulness faithfulness score 5 relevance relevance score 5 print f faith reason faithfulness reason 100 print summary statistics faith_scores r faithfulness_score for r in evaluation_report rel_scores r relevance_score for r in evaluation_report print f average faithfulness sum faith_scores len faith_scores 2f print f average relevance sum rel_scores len rel_scores 2f print f questions with faithfulness 3 sum 1 for s in faith_scores if s 3 each question triggers two api calls to gpt 4o one per judge for our eight test questions that s sixteen evaluation calls total which is manageable in production with thousands of daily queries you d want to batch these run them asynchronously and probably only evaluate a sample rather than every single response analyzing results and iterating the numeric scores give you an overview but it s the reasoning that tells you what to actually fix find problematic responses print low faithfulness score 4 for r in evaluation_report if r faithfulness_score 4 print f nq r question print f score r faithfulness_score print f reason r faithfulness_reason print f answer preview r answer 150 print n low relevance score 4 for r in evaluation_report if r relevance_score 4 print f nq r question print f score r relevance_score print f reason r relevance_reason the international orders question and the sale items question will almost certainly score low on faithfulness because the knowledge base doesn t address those topics and the model is likely to have improvised an answer the defective laptop question is interesting too because it requires synthesizing information from the general refund policy 30 days with the electronics defect clause 90 days for defective items with proof and depending on which chunks get retrieved the model may or may not have the complete picture what you do with the results depends on what you discover low faithfulness on certain question categories usually points to one of two things either the retriever is pulling in the wrong chunks a retrieval problem or the generator is going beyond the context it was given a generation problem looking at the retrieved context alongside the answer tells you which one you re dealing with low relevance scores on the other hand usually mean the system prompt needs tuning or the retrieved context is so far off topic that the model has nothing useful to work with for the operational side of running evaluations as part of your de ployment pipeline the llmops concepts cour se covers the infrastructure and workflow patterns using deepeval for structured evaluation writing custom judge functions works but for production use you probably want a framework that handles the boilerplate deepeval is one of the more mature options and it comes with well tested metric implementations that cover the most common evaluation criteria from deepeval import evaluate from deepeval test_case import llmtestcase from deepeval metrics import faithfulnessmetric answerrelevancymetric configure metrics faithfulness_metric faithfulnessmetric threshold 0 7 model gpt 4o include_reason true relevance_metric answerrelevancymetric threshold 0 7 model gpt 4o include_reason true create test cases from our rag results test_cases for result in eval_results test_case llmtestcase input result question actual_output result answer retrieval_context result context test_cases append test_case run evaluation evaluate test_cases test_cases metrics faithfulness_metric relevance_metric what you get from deepeval that s tedious to build on your own retry logic for when the judge returns malformed json which happens more often than you d think result caching so re running an evaluation doesn t double your api bill pytest integration that lets you wire llm evaluations directly into your ci cd pipeline each metric also generates a self explanation which means every score comes with a written breakdown of the judge s reasoning that you can inspect when something looks off for a deeper look at deepeval s full capabilities see evaluate llms effectively using deepeval llm as a judge best practices getting an llm judge to return numbers is straightforward getting those numbers to actually mean something useful for your team requires more care than most people expect going in evaluation frameworks and metrics besides deepeval there are several frameworks to choose from at this point and the landscape keeps growing here s a practical comparison based on where ea ch one fits best framework best for llm judge support rag specific metrics integration deepeval full evaluation suite ci cd integration yes with self explanatory scores faithfulness contextual precision recall relevancy pytest langchain ragas rag pipeline evaluation specifically yes faithfulness answer relevance context precision context recall langchain llamaindex mlflow experiment tracking with evaluation yes built in can also be combined with deepeval ragas via third party integrations mlflow ecosystem evidently production monitoring and drift detection yes with continuous tracking via custom evaluators monitoring dashboards langsmith langchain native tracing and evaluation yes via custom evaluators langchain for rag systems four metrics tend to cover most of what you need in practice faithfulness tells you whether the generated answer stays grounded in the retrieved context if the score comes back at 0 6 that means roughly 40 of the claims in the answer lack support from the provided documents this is your primary hallucination detector and in my experience it s the single most important metric to track on an ongoing basis answer relevancy captures whether the response actually addresses what was asked which is a separate issue entirely a response can be perfectly faithful to the context every claim checks out and still miss the p...