RAG Evaluation Metrics Explained - Full Breakdown & Transcript

[00:07] an answer in seconds. The response is clear, confident, and sounds completely correct. But, there is one problem. The information it used was wrong. And that leads us to one of the biggest challenges in building AI applications

[00:20] today. Now, how do we know whether we can actually trust the answer? This becomes especially important when we work with retrieval augmented generations or rag. Rag has become a popular approach for building AI

[00:32] applications because it allows a language model to retrieve information from external sources before generating a response. Instead of relying entirely on what the model learned during training, we can connect it to company

[00:44] documents, PDF databases, knowledge-based product documentation, research papers, and other resources for the information. But, adding retrieval doesn't automatically make an AI application reliable. The system might

[00:58] retrieve the wrong document. It might retrieve only a part of the information. The model might misunderstand the retrieval context. Or it might even generate information that was never present in the context at all. That is

[01:10] exactly why rag evaluation is so important. In this video, we are going to understand how to evaluate the complete rag pipeline. We look at retrieval metrics, generation metrics, and also the RAGAs framework, different

[01:22] evaluation approaches, popular evaluation tools, a practical evaluation workflow, and some of the common challenges you need to watch for. By the end, you'll understand not just whether a rag system is performing well, but

[01:35] also how to identify where it is failing and what you can improve. Now, if concepts like rag, LLM applications, prompt engineering, and also agentic AI are areas you want to explore beyond this video, you can check out Simply

[01:48] Learn's Apply Generative AI specialization. The program takes you beyond simply asking generative AI tools and and focuses on actually building AI-powered applications. You'll explore concepts such as advanced prompt

[02:00] engineering, retrieval augmented generation, LLMs, fine-tuning, and also agentic AI framework, along with hands-on projects designed around real-world applications. The learning path also covers advanced generative AI

[02:12] models and also architectures, building LLM applications, agentic AI frameworks, image generation, generative AI governance, and also a capstone project. And importantly, the program is designed around practical implementations. For

[02:25] involves building an AI-powered business intelligence assistant using LangChain, RAG, and LLMs, while others focuses on building an AI-powered HR chatbot that retrieves answers from policy documents. So, if you want to move from

[02:40] actually building generative AI applications, you can check out the description box below and in the comment section below. Before we start, here's a quick question for you to answer. A RAG system finds the correct documents, but

[02:55] the final answer contains information that was not present in those documents. Which problem does this present? Is it poor retrieval speed, low context recall, lack of faithfulness or grounding, incorrect user query

[03:08] the comment section below. So, let's begin with the foundation. Before we start evaluating a RAG system, we first need to understand exactly what is happening inside a pipeline. The process begins with a user question. Imagine an

[03:23] employee ask an internal AI assistant, "What is our company's work-from-home policy?" In traditional language model setup, an model may attempt to answer based primarily in on information learned during training. But that

[03:38] creates an obvious problem. The model may never have seen this organization's internal policy or policy may have changed after the model was trained. A RAG system approaches the problem differently. The second stage of our

[03:51] differently. The second stage of our slide is retrieve context. The system searches an external knowledge source and tries to find information relevant to user's question. That knowledge source might contain company's

[04:03] documents, PDFs, web pages, database records, or even other information. company's official work-from-home policy. We then go into the third stage, which is generate answer. The user questions

[04:16] and retrieve context are passed to the language model. The model uses that information to construct this response. Ideally, instead of inventing a policy, generate an answer based on direct on retrieved document. And that brings us

[04:31] to the fourth stage, which is evaluate. This is where our topic begins. We need to determine whether the retrieved information was relevant, whether it's important information was missed, whether the generated response was

[04:45] supported by the retrieved evidence, and whether the final answer was actually correct. So, when you think about rag evaluation, remember that there are essential two major areas we need to investigate: retrieval and generation. A

[04:59] fluent answer isn't enough. A trustworthy rag answer needs to be supported by right evidence. With that foundation in place, let's begin with the first half of the pipeline, which is retrieval evaluation. Before judging

[05:12] whether the final AI response is good, we need to determine whether the system retrieved good information in the first place. That is the purpose of retrieval evaluation. There are four concepts

[05:24] highlighted on the slide, which is context precision, context recall, retrieval precision, and retrieval recall. Let's start with context precision. Context precision essentially ask, "Of

[05:36] the information retrieved by the system, how much of it was actually relevant?" Imagine a user ask about a company's refund policy. The system retrieves 10 pieces of text, but only three of them actually discuss

[05:50] refunds. The other seven might discuss shipping, warranties, payments, and also completely unrelated topics. The system technically retrieved information, but the retrieved context contains a lot of noise.

[06:04] That is where precision becomes important. Now, let's look into context recall. Recall looks at the problem from another direction. Instead of asking how much of the retrieved information is relevant, we ask how much of the

[06:17] knowledge base did the system successfully retrieve? Suppose five important pieces of information are needed to answer a question correctly, but the retrieval finds only two. The information it found may be excellent,

[06:32] but the context is incomplete. Retrieval precision and retrieval recall follow a similar principle. When we were examining retrieval chunks or documents, retrieval precision tells us whether the chunks returned by the retriever are

[06:45] focused and useful. Retrieval recall tells us whether the retriever found all the important chunks that should have been available. An easy way to remember the difference is precision ask how good is what I received. How good is what I

[07:00] retrieved? Recall ask how much of what I needed did I find? The distinctions matter because poor retrieval creates problem downstream. If the context is noisy, the language models may become distracted. If important information is

[07:14] missing, the model may produce an incomplete answer. And if completely incorrect information is retrieved, the model may confidently generate an incorrect response. So, before blaming the language model for a poor rag

[07:28] answer, always inspect the retrieval layer first. Now that we have evaluated what goes into the model, let's evaluate what comes out of it. Once the system has retrieved information, the language

[07:40] model uses that context to generate the final response. Now, our evaluation focuses on changes. Instead of asking, "Did we retrieve the right information?" we ask, "Did the model generate the right answer using that information?"

[07:54] The first important metric is list, also called as groundness. generated answer is actually supported by the retrieved context. For example, suppose the retrieved document says, "Customer may request a refund within 30

[08:09] days of purchase." The model response, "Customers can request a refund within 30 days." That answer is faithful because it is directly supported by the context. But suppose the model adds, "Premium

[08:22] customers receives an additional 15 days." If that information wasn't present in the retrieved context, the model has introduced unsupported information. That is a grounding problem and also a potentially a hallucination.

[08:36] The second metric is answer relevance. Here we ask whether the generated response actually addresses the user's question. If the user asks how to reset a password and the assistant gives long explanation about cybersecurity best

[08:50] practices, the information may be useful, but it doesn't directly solve the user's problem. The third metric is answer correctness. Correctness asks whether the answer is factually right when compared with a

[09:03] trusted reference answer or ground truth. These dimensions are related, but they are not identical. An answer can be relevant, but incorrect. It can correct, but poorly grounded. And it can be grounded in a retrieved context while

[09:18] questions. The quality scales shown on the slide helps us think about this as a spectrum. At one end, we have poor answers that may be irrelevant or hallucinated. Then we have a partially relevant response.

[09:33] As quality improves, the answer becomes more grounded and complete. And at the highest level, we want an answer that is faithful, relevant, and also correct. So, retrieval evaluation tells us whether the system found good evidence.

[09:47] Generation evaluation tells us whether the model used that evidence properly. The next challenge is managing all of these matrices symmetrically, and also that's where the framework such as RAGAS becomes useful. Evaluating one RAG

[10:01] response manually is relatively easy, but imagine evaluating hundreds and thousands of questions every time you change your retriever prompt, model, or even knowledge base. At this scale, we need more systematic

[10:14] approach. One framework commonly used for RAG evaluation is RAGAS. RAGAS helps us evaluate important aspects of RAG pipeline using metrics such as the four shown on the slide. We have

[10:28] faithfulness, answer relevancy, context precision, and also context recall. Now, what's useful about this combination is what it covers both major parts of RAG architecture. Context precision and context recall helps us understand the

[10:41] quality of retrieval. Whereas an answer relevance help us understand the quality of generation. Now, look at the pipeline shown on the slide. The first stage is retrieve. The system searches for relevant documents. Repeat. The second

[10:55] stage is augment. The retrieved information is added to the prompt as context. The third stage is generate. The language model produces the answer, and finally we evaluate the results. This is where RAGAS helps us convert

[11:10] qualitative behavior into measurable signals. For example, imagine our faithfulness score is high on our context recall score is low. That tells us something very useful. The language model is probably using the information

[11:23] it receives correctly, but the retriever isn't finding enough of the information needed to answer the question completely. Now, consider the opposite situation. The context precision and context recall are strong, but faithful

[11:37] is poor. In that case, retriever may be working correctly, but the language model is adding unsupported information. This is why individual metrics are often more useful than simply having an overall score.

[11:50] The goal isn't just to say, "My rag system scored 80%." The real goal is to identify which part of the pipeline needs improvement. Ragas gives us more structured way to do that, but metrics and frameworks are the only part of the

[12:04] story. We also need to decide how the evaluation itself should be performed. Now, there are three broad approaches we can use while evaluating rag systems. Automatic evaluation, human evaluation, and also hybrid evaluation. Let's begin

[12:18] with automatic evaluation. Automatic evaluation uses software, predefined metrics, or even language models to score responses. Its biggest advantage is scale. Imagine you have 10,000 test questions. Having a

[12:31] human manually evaluate every response would take a significant amount of time Automated evaluations allows us to repeatedly run test whenever we modified our application. This makes it particularly useful during

[12:45] development and regression testing. The second approach is human evaluation. Humans can understand nuances that automate matrices sometimes struggle to capture. For example, an answer may technically contain the correct facts,

[12:59] but still be confusing, misleading, poorly worded, or even inappropriate for the intended audience. A subject matter expert can also identify domain-specific miss. That makes human evaluation particularly

[13:16] valuable when quality and nuance are extremely important. The third option is hybrid evaluation. This combines automated scoring with human review. In many real-world applications, this is one of the most practical strategy. We

[13:30] might automatically evaluate thousands of responses, identify suspicious or low-scoring examples, and then send a small subset of expert review. This gives us the speed of automation while retaining human judgment where it

[13:44] matters most. So, the key message from this slide is there is no single perfect evaluation method. Automatic evaluation gives a scale, human evaluations gives us new essence, hybrid evaluation gives us balance of both. The right choice

[13:58] depends on the application stage, cost, risk, and also required level of the different evaluation approach, let's return briefly to retrieval and visual precision and recall more clearly. Now here we'll give a more visual way of

[14:15] understanding the retrieval metric we introduced earlier. Let's begin with context precision. Imagine each block of represents a piece of retrieval information. Some blocks contain information relevant to the question,

[14:28] while others contain noise. Context precision ask what proportion of the retrieved information is actually relevant. If almost everything we retrieved is useful, precision is high. If we retrieve large amounts of

[14:42] unrelated information, precision falls. Now look at the context recall. all the blocks represented the information that should ideally be available to answer the question. Recall measures how much of the relevant

[14:57] found. If important information remains missing, the recall is low. Retrieval precision and retrieval recall apply the same basic logic when we are evaluating the actual chunks of documents returned

[15:12] by the search system. This creates an important engineering trade-off. Suppose we configure the retriever to return only one document, that document might be extremely relevant. So, precision could be excellent, but there may be

[15:27] four other important documents that we completely missed. Our recall would therefore be poor. Now, suppose we retrieve 50 documents, we might capture every single relevant document giving us excellent recall. But, if most of those

[15:42] 50 documents are irrelevant, we are introduced enormous amounts of noise and This is why simply increasing the number of retrieval chunks doesn't automatically improve a rag system. We want the retriever to find enough

[15:56] information without overwhelming the model with unnecessary context. And this is particularly important because context window are not unlimited resources. More context can increase cost, latency,

[16:09] and also distraction. So, the goal is not maximum retrieval, the goal is high-quality retrieval. That's why the key takeaway shown is good retrieval is foundation of a good rag answer. Once we know which metrics

[16:23] we want to measure, the next practical question is which tools can help us do it. There are several tools and frameworks available for evaluation of rag and LLM. And they don't all solve exactly the

[16:35] same problem. The first one shown here is Ragas. As we already discussed, Ragas focus specifically on rag evaluation and provides metrics for examining both retrieval and also generated responses. That makes it useful starting point.

[16:50] When we want structured rag specific evaluation, next is TruLens. TruLens focuses on evaluating and observing LLM applications. It can help developers understand application behavior and also inspect the quality of different

[17:04] Then we have LangSmith. LangSmith is particularly useful for tracing, evaluating, experimenting, and also monitoring LLM powered work. Tracing becomes important because modern rag systems can contain many stages.

[17:18] A question may go through re-return, retrieval, re-ranking, prompt construction, model generation, and also post-processing. If something fails, tracing helps us understand exactly where the failure occurs. Next is deep.

[17:32] Deep Eval provides an evaluation and testing framework that can be incorporated into development workflows, making it useful when teams want evaluation to behave more like software testing. And finally, we have customer

[17:45] or in-house metrics. This become extremely important in production application. Generic metrics cannot understand every organization's definitions of success. For example, a financial rag assistance may require

[17:59] strict numeral accuracy. A legal applications may care heavily about citation correctness. A customer support applications may care about a policy compliance. This is why the workflow shown here

[18:12] possibilities. We might start with ragas for core metrics, use trulens or langsmith to debug and observe the behavior, use deep eval for structured testing, and then extend the evaluation process with

[18:26] metric design specifically for our one own application. The key takeaway is no single tool does everything. Choose tools based on what you're trying to measure rather than trying to focus on one framework to solve every evaluation

[18:41] problem. Now, let's turn these idea into practical workflow. Now, let's look at what rag evaluation might actually look like during development. The workflow begins with create an evaluation data set. This is one of the most important

[18:54] stages. We need a representative collections of questions of what users are likely to ask. Where possible, we should also include expected answers and relevant ground truth context. The quality of evaluation data sets directly

[19:09] affects the quality of our evaluation. If our test questions are unrealistic, the scores may look excellent while the production application still performs poorly. The second stage is run evaluation using tools.

[19:23] Here we select the metric that matters for our application. For retrieval, that might include context precision and context recall. For generation, we might also correctness. Then we run a data set through an

[19:38] evaluation framework. The third stage is to analyze the results. This is where evaluation becomes actionable. We don't simply look at the score and decide whether it is good or bad. We investigate patterns, maybe questions

[19:52] involving tables consistently failing, and also maybe faithfulness is excellent, but context recall is weak. Maybe one document category performs much worse than others. Those patterns help us identify root causes. Then we

[20:06] reach improve your pipeline. If retrieval is a problem, we might change the chunk size, embedding model, search strategy, metadata filters, or even re-ranking approach. If generation is a problem, we might improve the

[20:20] prompt, change the model, adjust instructions, or even improve the context supplied to the model. Then we run the evaluation again. That's why this isn't a linear process. It is an iterative loop. Measure, analyze,

[20:33] improve, and re-evaluate. Over time, this creates a measurable development process instead of relying only on a few manually selected examples that happened to work. But even with strong workflow, rag evaluation still comes with some

[20:47] important challenges. One of the first challenges is shown on the slide is hallucination. A language model can produce an answer that sounds confident information that isn't supported by retrieval context. This is particularly

[21:03] dangerous because the incorrect answers are not always obviously incorrect. The second challenge is poor retrieval. If irrelevant or incomplete information enters the generational stage, answer quality is likely to suffer. This is why

[21:18] separating retrieval evaluation from generation evaluation is so important. Otherwise, we might incorrect blame the language model for a problem caused by search. The third challenge is is wrong answer despite good retrieval. This is

[21:34] the opposite situation. The retrieval finds exactly the right document, but the language model misunderstands it, ignores an important condition, or even combine the information incorrectly. For example, the context might say, "Refunds

[21:48] are allowed within 30 days only for unopened products." The model might respond, "All products can be returned within 30 days." The retrieval way was correct, but the generation lost an important condition.

[22:02] The fourth challenge is is evaluation biases. Metrics are not automatically objective simply because they produce numbers. The test data set may over present easy questions. A model-based evaluator may

[22:16] prefer a certain writing style. A preference answer may itself be incomplete, or even the evaluation criteria may not reflect what real users actually need. The evaluation should never become a blind pursuit of highest

[22:31] Scores should help us understand system behavior. The goal is to combine metrics, examples, traces, and where necessary human judgment to understand what is actually happening. Once we recognize these challenges, we can bring

[22:46] everything together into final life cycle. The final slide summarizes the complete physiology behind rag evaluation. We begin with build. We create the rag pipeline, our document retrieval system prompts model, and also

[23:00] the application logic. Then we evaluate and importantly we evaluate both retrieval and generation. We measure whether the right information was found and whether the model used that information correctly. The third stage

[23:13] is analyze. This is where we move beyond the score. We identify gaps, examine failed examples, inspect traces and also try to understand the root causes. the important chunk missed? Was the correct context provided but

[23:30] misunderstood? Did the model hallucinate additional information? Once we understand the cause, we move to improve. We might refine retrieval, modify prompts, change chunking, improve and data set, induce re-ranking, change

[23:44] models, or also create new guardrails and then the cycle starts again. This loop matters because the RAG system is never truly finished. The knowledge base changes. New documents are added. Users begin asking questions we didn't

[24:00] anticipate. Models are upgraded, prompts evolve, business requirements change. So evaluation should not only be treated as a final step after development. It should be a part of development life cycle itself. And that brings us to the

[24:14] central goal shown at the bottom of the slide, which is continuously repeat. Which is continuously measure, learn, and improve to deliver the accurate and reliable responses. That is the ultimately what good RAG evaluation

[24:29] gives us. Not just collection of scores, but a structured way to build AI applications that become more reliable over the time. So let's bring everything together. We started by understanding the basic RAG pipeline, retrieve

[24:41] information, provide that information as context, generate an answer, and also evaluate the result. We then learned that RAG evaluation has two major quality. Metrics such as context precision and context recall help us

[24:55] determine whether we are finding the right information without introducing too much noise or missing important evidence. The second is generation quality. Metrics such as faithfulness, answer relevance, and also answer

[25:08] correctness help us determine whether the language model actually used that evidence properly. We then explored RAG's different evaluation approaches, popular tools, a practical evaluation workflow, and a common challenges that

[25:21] can affect evaluation quality. But, there is one idea that I want you to remember after this video. A RAG system should not only be considered good simply because its answers sound good. A reliable RAG system is one where we can

[25:34] understand where the information came from, whether the right information was retrieved, whether the model used is correct, or why the system failed when something went wrong. So, as you build RAG applications, don't stop when the

[25:47] chatbot starts producing impressive responses. Evaluate the retrieval, evaluate the generation, analyze the failures, improve the pipeline, and then evaluate it again. Because in production AI systems, the goal isn't simply to

[26:00] generate an answer. Do like this video and subscribe to Simplilearn for more such videos. Until next time, thank you for watching, and keep learning with for watching, and keep learning with Simplilearn.