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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):
and technology ai agents ai news artificial intelligence aws azure business intelligence chatgpt databricks dbt docker excel generative ai git google cloud platform hugging face java julia kafka kubernetes large language models mongodb mysql nosql openai postgresql power bi pyspark python r scala snowflake spreadsheets sql sqlite tableau category topics discover content by data science topics ai for business big data career services cloud data analysis data engineering data literacy data science data visualization datalab deep learning machine learning mlops natural language processing vector databases browse courses category home tutorials artificial intelligence llm as a judge a complete guide with hands on rag example 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 list apr 20 2026 15 min read group training more people get your team access to the full datacamp for business platform for business for a bespoke solution book a demo your rag pipeline returns an answer to a user s question it looks reasonable reads well and sounds confident but when you compare it against the documents that were actually retrieved you start noticing things like a date that doesn t appear anywhere in the context or a recommendation that contradicts what the source material says catching those problems at scale is where things get complicated this is precisely why metrics like bleu and rouge were built for machine translation and summarization tasks but they can t tell you whether the model s answer is actually grounded in the retrieved context or whether it contradicts the source human evaluation catches those subtleties but is too expensive at scale think of 10 000 queries or more so there s a gap automated metrics are fast but shallow while human review is thorough but expensive and slow using an llm as a judge sits in between you take a capable model hand it a rubric you ve written and ask it to evaluate the outputs of your system the way a human reviewer would it s not a perfect solution i ll get into the failure modes later but it gives you a practical way to monitor quality at a scale that neither traditional metrics nor manual annotation can match on their own in this tutorial i will walk you through building an llm judge to evaluate a rag system with working code throughout so you can reproduce everything on your own machine what is llm as a judge the main idea of llm as a judge is asking a language model to evaluate text outputs based on criteria you spell out in a prompt so basically you are using an llm to check if the content generated by another model is good or not the reason this works at all and i remember being skeptical of it initially is that evaluation is a fundamentally easier task than generation when a mo del generates a response from scratch it s juggling a lot of competing constraints at once accuracy tone instruction adherence staying grounded in context not over committing on uncertain information but when you hand that same model a finished response and ask it to check specific properties the task becomes much more focused as it only needs to assess not create and mod els tend to perform more consistently on that narrower task rag with langchain integrate external data with llms using retrieval augmented generation rag and langchain explore course comparing llm as a judge approaches in practice teams tend to land on one of these approaches depending on what they re trying to measure direct scoring has the judge rate a single output on a numeric scale 1 to 5 1 to 10 whatever your rubric defines you get a number and if you ve asked for it a written explanation of the reasoning this is the most common starting point though it has a calibration problem i ll come back to pairwise comparison puts two candidate responses side by side and asks the judge to pick the better one this gets around the numeric calibration issue entirely because the judge only needs to make a relative call not assign an absolute number teams often reach for this when they re comparing prompt variants or model versions against each other reference based evaluation provides the judge with both the model output and a gold standard answer and asks how well the output matches this works best for factual q a and structured outputs where you already have labeled ground truth to compare against binary classification strips the judgment down to pass or fail on one specific property is this response faithful to the context does it contain personally identifiable information is it positive these binary checks are faster to run cheaper per evaluation and tend to produce more consistent results than the numeric approaches image by author four different approaches of llm as a judge for a direct overview of strengths use cases and restraints i have compared all four approaches in the following table approach when to use strengths watch out for direct scoring general quality monitoring continuous tracking easy to trend over time works with single outputs judges drift in how they calibrate scores pairwise comparison a b testing models prompt variant comparison more reliable rankings than absolute scores doubles your api calls doesn t give an absolute quality signal reference based factual q a structured outputs clear ground truth makes evaluation straightforward requires labeled data penalizes valid alternative phrasings binary classification safety checks hallucination detection compliance low ambiguity easy to automate alerts loses nuance on borderline cases for a broader look at evaluation approaches beyond the judge pattern llm benchmarks explained covers the full picture how does an llm judge work under the hood it s a simple api call as you just need to package up the content you want evaluated the model s output the original user query and any retrieved context that was used during generation and wrap it in a prompt that tells the judge model what you care about and how you want the results formatted the judge processes this and returns a structured response usually a score along with written reasoning that explains why it assigned that score the quality of your evaluation depends almost entirely on how well you write the rubric i cannot overstate this a simple prompt that says rate this response from 1 to 5 will give you scores that are all over the place because the judge has no shared understanding of what a 3 means versus a 4 this is why you need to spell out concretely what each score level looks like and include examples if you can implementing llm as a judge now for the hands on part we re going to build a complete evaluation pipeline fro m scratch a retrieval augmented generation rag system that answers questions from a small knowledge base a set of test queries designed to trigger different failure modes and an llm judge that scores the outputs on faithfulness and answer relevance setting up the environment you ll need python 3 9 and an openai api key which you can get in the openai console insta ll the dependencies pip install openai chromadb langchain langchain openai langchain community deepeval set your api key import os os environ openai_api_key your key here we re using openai for both the rag generator and the llm judge though they ll be different models gpt 4o mini for generation gpt 4o for judging for embeddings text embedding 3 small does the job for a tutorial scope in a production system you d want to benchmark a few embedding models on your specific domain data before committing to one if you want to build up more background on rag before jumping into the evaluation code our retrieval augmented generation rag with langchain course walks thr ough the fundamentals preparing data and vector store we need a small knowledge base for the rag system to retrieve from i m going to use a set of text chunks about a fictional company s return policy the content itself isn t the focus here but having a contained set of documents makes it easier to see exactly when the model goes beyond the provided context which is the failure mode we want the judge to catch from langchain_openai import openaiembeddings from langchain_chroma import chroma from langchain schema import document sample knowledge base documents document page_content all customers are eligible for a full refund within 30 days 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...
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